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Vol. 06, No. 02, pp. 342 –357 (2025)
ISSN: 2708-0757
JOURNAL OF APPLIED SCIENCE AND TECHNOLOGY TRENDS
www.jastt.org
342
doi: 10.38094/jastt62404
A Hybrid LLM–Knowledge Graph Framework for Accurate
Biomedical Question Answering
Havraz Y. Omar1,²*, Abdulhakeem O. Mohammed³
¹Department of Information Technology, Technical College of Duhok, Duhok Polytechnic University, Duhok, Kurdistan Region,
Iraq. havraz.omar@dpu.edu.krd
² Department of Information Technology, Technical College of Informatics – Akre, Akre University for Applied Sciences, Akre,
Kurdistan Region, Iraq.
³ Department of Computer Science, College of Science, University of Zakho, Zakho, Kurdistan Region, Iraq.
a.mohammed@uoz.edu.krd
*Correspondence: havraz.omar@dpu.edu.krd
Abstract
Biomedical question answering requires accurate and interpretable systems; however, existing approaches often face challenges such as
language model hallucinations and limited reasoning when relying solely on standalone knowledge graphs. To address these limitations,
this study proposes a hybrid framework that integrates the LLaMA-3B language model with a Neo4j-based drug–disease–symptom
knowledge graph. The system translates natural language questions into executable Cypher queries, operates on an iBKH-derived graph
comprising over 65,000 entities and 3 million relationships, and returns answers with supporting evidence through a transparent interface.
Experiments conducted on 60 biomedical questions across three levels of difficulty demonstrate the robustness of the approach: 96%
exact match for simple queries, 95% for medium queries, and 86.7% for complex queries. Overall, the system achieves Precision@5 of
96.1%, Recall@5 of 89.0%, F1@5 of 91.0%, Hits@k of 96.1%, and an MRR of 94.4%, while maintaining an average response time of
only 6.07 seconds. These results indicate that the system retrieves nearly all relevant answers, ranks them correctly, and delivers them
with latency low enough for interactive use. Moreover, unlike cloud-based APIs such as ChatGPT, which require internet connectivity
and external data transmission, the proposed framework operates fully offline, ensuring privacy, reproducibility, and compliance with
biomedical data governance. Overall, this pipeline provides an accurate, efficient, and privacy-preserving solution for biomedical question
answering, making it a practical alternative to cloud-dependent approaches in sensitive healthcare contexts.
Keywords: Knowledge Graph, LLM, Question Answering, Neo4j, Biomedical Informatics, Healthcare AI, LLaMA 3.
Received: August 14th, 2025 / Revised: October 10th, 2025 / Accepted: October 16th, 2025 / Online: October 20th, 2025
I. INTRODUCTION
Answering questions in the biomedical field is a difficult task
due to the complexity of medical knowledge and the need for
precision. In recent years, large language models (LLMs) like
LLaMA, GPT-4 have made progress in understanding and
generating human-like responses to medical questions [1, 2].
These models can process large amounts of information and
respond in natural language, which makes them helpful in
healthcare settings [3]. However, they often struggle to provide
accurate answers when dealing with specialized biomedical
content [4, 5].
One major issue with LLMs is a problem called hallucination,
where the model generates information that sounds right but is
actually incorrect or unsupported [6]. In medical applications,
this can be dangerous, as healthcare professionals rely on precise
and trustworthy information [7]. Therefore, researchers are
exploring ways to combine LLMs with structured sources of
knowledge to improve their reliability [8].
LLM-only systems in biomedicine still hallucinate and are
hard to verify, limiting safe use [9, 10]. Biomedical knowledge
graphs (BKGs) such as iBKH and SPOKE curate multi-source
facts and enable multi-hop reasoning, yet they neither interpret
free text nor generate answers [11, 12]. Recent hybrids (KG-
aware RAG) improve grounding but often lack explicit path-level
justifications and robust end-to-end answer evaluation [13, 14].
Recent studies have increasingly integrated Knowledge
Graphs (KGs) with Large Language Models (LLMs) to improve
factual accuracy, reasoning, and reduce hallucinations. Notable
examples include DR.KNOWS, which combines UMLS-based
KGs with LLMs for better diagnostic reasoning [15], KnowNet | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
343
for visualizing and validating LLM outputs [16], and MedKA for
KG-enhanced question answering [17].
To address these challenges, several recent works have
explored the integration of large language models with
biomedical knowledge graphs (KGs). A biomedical KG is a
structured network that connects entities such as diseases, drugs,
and symptoms using defined relationships [18, 19]. These graphs
store verified medical knowledge from trusted databases,
allowing for more accurate and explainable responses [12]. KGs
are especially useful in multi-step reasoning tasks, where finding
an answer requires connecting different pieces of information
[20]. These entities and relationships can be visually represented
in a biomedical knowledge graph, as shown in Fig. 1, where
nodes represent medical concepts such as drugs, diseases,
symptoms, and pathways, and edges denote their semantic
relationships.
Fig. 1. Overview of Biomedical Knowledge Graph Entities and Relationships
One example of a widely used biomedical KG is SPOKE,
which includes millions of nodes and relationships from over 40
biomedical databases [12]. Integrating an LLM with a KG allows
the strengths of both technologies to work together: the LLM
provides language understanding, and the KG provides
structured, factual knowledge [21, 22]. A common method is
retrieval-augmented generation (RAG), where the LLM retrieves
information from the KG and uses it to generate more accurate
responses [13, 23]. In more advanced setups, the LLM can even
generate queries like Cypher to fetch specific data from the graph
[24, 25]. Neo4j is a popular graph database that supports fast and
flexible storage and querying of knowledge graphs using Cypher
[21]. It is well-suited for biomedical applications because it
allows easy exploration of complex medical relationships. Recent
work has shown that combining Neo4j with LLMs can lead to
better accuracy, fewer hallucinations, and more explainable
results [24, 26].
Despite improvements, building a reliable hybrid system that
combines an LLM with a biomedical KG remains a technical
challenge. Some approaches require complex pipelines or large
training datasets, while others rely on fine-tuning specific to a
narrow set of questions [27, 28]. There is still a need for systems
that are both accurate and easy to scale, especially in domains like
medicine where the cost of errors is high [22].
Recent advances in KG-augmented LLMs have improved
performance, yet biomedical QA continues to face three practical
gaps:
1. Traceability: LLM-only or text-retrieval-only pipelines
rarely provide graph-grounded justifications; users lack the
ability to inspect the exact nodes and edges that support an
answer.
2. Evaluation: Prior work often judges quality via surface-
form checks (e.g., matching a Cypher template), which fails to
capture end-to-end answer correctness or ranking quality
across different difficulty levels.
3. Deployment: Many solutions assume cloud resources or
domain-specific fine-tuning, yet biomedical contexts typically
demand a local, privacy-preserving system with low latency
and reproducible behavior.
Timestamp-aware execution and periodic KG refresh help
avoid deprecated or contraindicated links, making the tool better
suited for safety-critical clinical contexts (e.g., drug–drug
interactions).
To address these limitations, Our work introduces a locally
deployable pipeline that translates biomedical questions into
executable Cypher queries over a Neo4j knowledge graph. The
system returns answers with supporting nodes and edges, and is | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
344
evaluated using Exact Match, Precision, Recall, F1, Hits@k,
MRR, and latency across simple, medium, and complex question
sets. Unlike prior template-based methods, our approach enables
traceable, outcome-level validation. In summary, the main
contributions of this work are as follows:
• Hybrid LLM to Cypher QA: A system that translates
natural language questions into accurate, executable
Cypher over a Neo4j drug, disease, and symptom KG.
• Prompt-driven query generation: Schema, entity
aware prompting that reliably maps diverse biomedical
questions to graph queries.
• Evidence transparency: Along with each answer, we
surface the generated Cypher and the supporting
justification subgraph (nodes, edges) plus a brief natural
language rationale.
• Answer-level evaluation: End-to-end assessment using
Exact Match, F1, Precision/Recall, Hits@k, MRR and
latency across simple, medium and complex tiers.
• Local, reproducible deployment: On-prem LLaMA 3
inference (no cloud dependency) suitable for biomedical
settings requiring low latency and strong data control.
The remainder of this paper is organized as follows: Section
2 reviews related work on biomedical knowledge graphs and
LLM-based QA systems. Section 3 provides background on
knowledge graphs, large language models, and question
answering frameworks. Section 4 details the proposed
methodology,
including
system
architecture,
dataset
construction, and query translation. Section 5 presents the
experimental results through both quantitative metrics and
qualitative case studies. Section 6 discusses the findings, analyzes
limitations, and compares performance against baseline models.
Finally, Section 7 concludes this paper and outlines directions for
future work.
II. RELATED WORK
Recently, studies have concentrated on the integration of
clinical and medical knowledge graphs (LLM) to improve the
answer to medical questions. Researchers have derived several
biomedical KGs using Neo4j and incorporated the application of
LLMs like LLaMA and GPT to convert natural language
questions into graph queries. Improvements in answer
correctness, reduction of hallucination errors, one-to-many
relationships, and support for complex reasoning were the
objectives of these efforts. Some frameworks also adopted
retrieval methods to ground responses in secure data.
Su et al.[11] developed an integrative Biomedical Knowledge
Hub (iBKH), a huge biomedical knowledge graph that comprises
18 of the very best data sources. The deployment of the iBKH in
Neo4j allows for a user-friendly web portal to allow fast and
interactive knowledge retrieval. The system implemented
advanced graph learning techniques to enable the discovery of
biomedical knowledge, illustrated by an example of repurposing
in silico drugs for Alzheimer’s disease. iBKH achieved
promising predictive performance for known drugs and proposed
possible new drug candidates.
Rajabi and Kafaie[19] proposed a disease knowledge graph
using a cross-referential disease database comprising diseases,
symptoms, and drugs interconnected with relationships. They
transferred the data into Neo4j to create a graph of 9,400 nodes
and 45,000 relationships representing the semantic links between
medical concepts. Applying Cypher queries enabled answering
complex medical questions regarding identifying drugs that may
cause certain diseases; it was demonstrated that the graph inferred
new relationships not explicitly existing in the original data. The
conclusion was that disease knowledge graphs sped up clinical
discovery and contributed to understanding complex medical
relationships.
Hou et al.[3] assessed and contrasted ChatGPT (both GPT-
3.5 and GPT-4) and the biomedical knowledge graphs (BKGs)
concerning their ability to answer biomedical questions, generate
new knowledge, and reason. Their datasets were focused on
dietary supplements and drugs, while evaluation criteria entailed
accuracy, novelty, and reasoning ability. The results indicate that
while GPT-4 surpassed GPT-3.5 and BKGs in knowledge
provision, it proved inconsistent with regard to citations and
reasoning. Compared to them, BKGs scored higher in accuracy
and reliability, especially in discovering novel links as well as
within structured reasoning.
Soman et al.[13] presented a novel framework called KG-
RAG that integrates a large biomedical knowledge graph
(SPOKE) with LLaMA 2, GPT-3.5, and GPT-4 (LLMs) to
produce accurate biomedical text. They optimized the retrieval of
relevant graph context to cut over 50% tokens without losing
accuracy. It aided LLMs in performing better on biomedical
question answering with very high accuracy boosts, especially in
the case of LLaMA 2. They compared KG-RAG to other retrieval
methods and indicated its comparatively more robust and
efficient results. The framework produced reliable evidence-
based answers grounded in biomedical knowledge.
Luo et al.[23] created ChatKBQA, a new framework with a
question-and-answer approach over knowledge bases that first
generates logical forms with the help of fine-tuned LLMs and
then retrieves the relevant entities and relations. This generate-
then-retrieve approach is supposed to handle a couple of issues
with the earlier methods concerning tedious retrieval and error
propagation. They fine-tuned open-source LLMs like LLaMA 2
to change natural-language questions into logical forms with high
accuracy. The retrieval phase uses unsupervised phrase-level
semantic matching in a way that enhances the alignment of
entities and relations. Experiments on benchmark datasets
indicate ChatKBQA to be superior to its predecessors, with the
highest accuracy to date.
Pusch and Conrad[6] conducted work under a hybrid
approach conflating LLMs and biomedical Knowledge Graphs
(KGs) to suppress hallucination errors in question-answering.
They proposed query-checking algorithms for validating,
correcting, and executing the KG Cypher queries that LLMs
generated, thereby attaining accurate and understandable
answers. The system used retrieval-augmented generation (RAG)
to ground answers within KG data. The methodology was
validated on a biomedical KG called PrimeKG using 50
benchmark questions, assessing models like GPT-4 Turbo and
LLaMA 3. Commercially available GPT-4 Turbo obtained | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
345
record-high accuracy, while open-source models achieved
impressive gains through prompt optimization.
Feng et al.[22] developed the Knowledge Graph-based
Thought (KGT) framework that integrated LLMs with a pan-
cancer knowledge graph for biomedical question answering.
KGT was designed to reason on the knowledge graph schema and
identify optimal subgraphs to use for directing accurate answer
generation, all without fine-tuning the LLMs. The framework is
benchmarked against a new dataset (PcQA) designed specifically
for pan-cancer KGQA tasks and has outperformed all existing
state-of-the-art approaches by a rather large margin. KGT’s
practicality in biomedical issues was highlighted through case
studies for drug repositioning, drug resistance, and biomarker
discovery. Their approach exhibited robust adaptability among
various LLMs.
Rezaei et al.[26] developed AMG-RAG, a dynamic
framework that utilizes autonomous LLM agents with medical
search tools in the continuous construction and real-time updating
of Medical Knowledge Graphs (MKGs). Their system
incorporated confidence scoring and multi-hop reasoning to
improve accuracy and interpretability in medical question
answering. AMG-RAG outperformed size models on both very
hard MEDQA benchmarks and more accessible MedMCQA
ones, proving that it could conduct efficient reasoning based on
current structured medical knowledge. They also used Neo4j to
manage the knowledge graphs while adding external searches to
ensure the latest data.
Tiwari et al.[24] presented Auto-Cypher, a recent automated
pipeline for producing high-quality synthetic data for training
LLMs by mapping natural language to Cypher queries for graph
databases like Neo4j. The pipeline deployed the novelty of LLM-
as-database-filler to synthesize Neo4j databases for the execution
of generated queries to ensure their correctness. A sizable dataset
called SynthCypher was created, spanning multiple domains and
complex queries, leading to a 40% improvement in LLM
performance on Cypher generation. The datasets were used to
fine-tune open-source models such as LLaMA, Mistral, and
Qwen, and the SPIDER benchmark was adapted for evaluation
purposes.
Mohammed et al.[29] proposed a hybridized GraphRAG
framework combining Neo4j-based UMLS knowledge graphs
with a vector store for medical textbooks to create an improved
U.S.M.L.E.-style clinical question-answering approach. The
project integrated symbolic reasoning from knowledge graphs
with semantic retrieval performed on text embeddings to enhance
relevance and accuracy via adaptive re-ranking and query
expansion. The system had the answers produced by GPT-4o-
Mini, with different prompting strategies encouraging evidence-
based and traceable responses grounded in verified medical
knowledge. Experiments showed that the hybrid approach
improved factual accuracy and citation fidelity as compared to
the L.L.M.-only approach, enhancing transparency and
reliability. It is shown that binding both structured and
unstructured medical knowledge sources could aid in
ameliorating hallucinations and hence improve clinical
trustworthiness in AI-driven medical QA.
Yang et al.[30] articulated sepsis knowledge graph was
crafted by combining multicenter clinical data from over 10,000
patients with the help of GPT-4 for entity recognition and
relationship extraction. Real-world data were collected from
three hospitals and integrated with clinical guidelines and
databases from the public domain. The knowledge graph
contained 1,894 nodes and 2,021 relationships pertaining to
diseases, symptoms, biomarkers, treatments, and complications.
GPT outperformed other models in every resolution on sepsis-
specific datasets to obtain high F1-score results. The constructed
graph highlighted complex interactions in sepsis for assisting
clinical decision-making and was implemented on Neo4j.
Guan et al.[20] proposed a novel method for constructing a
local knowledge graph from retrieved biomedical documents by
extracting propositional claims. They carried out layer wise
summarization on this graph to capture multi-document
relationships and provide comprehensive contextual information
to a language model for question-answering purposes. The
method resolved issues in multi-document biomedical QA, such
as noise cancellation and efficient context usage. They then tested
their method on several benchmarks for biomedical question
answering, achieving performance at least comparable to, if not
better than, existing retrieval-augmented generation (RAG)
baselines. The study established enhanced reasoning and answer
accuracy of the model achieved through structured graph
summarization.
Previous studies have improved biomedical QA using KGs
and LLMs, but important gaps remain. Most systems lack
transparent, graph-based justifications, rely on limited evaluation
methods, or depend on cloud resources that reduce privacy and
reproducibility. Our framework addresses these gaps by
providing visible Cypher queries with evidence subgraphs,
applying comprehensive performance metrics across difficulty
levels, and ensuring fully local, privacy-preserving deployment.
Table I summarizes key previous studies on biomedical
knowledge graphs and question answering, outlining their
methods, datasets, and main limitations.
III. PRELIMINARIES
This section outlines the fundamental concepts required to
understand the proposed system. It introduces biomedical
knowledge graphs, explains how Neo4j stores data in graph
form, and describes the use of Cypher for querying. It also
provides a brief overview of large language models (LLMs) and
their role in interpreting natural language.
A. Biomedical Knowledge Graphs
Biomedical Knowledge Graphs (BKGs) provide a structured
representation of complex biomedical information by modeling
diverse medical entities, such as diseases, drugs, symptoms, and
biological pathways, as interconnected nodes within a graph
structure. The edges in these graphs represent the semantic
relationships between these entities, including ’treats’, ’causes’,
’interacts with’ and many others, as illustrated in Fig 1. This
form of representation enables the integration of heterogeneous
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
346
scientific literature, clinical records, genomic databases, and
experimental findings [19, 31].
Such integration creates a comprehensive biomedical
knowledge base that supports advanced analytics and discovery.
For example, biomedical knowledge graphs can reveal previously
unknown relationships (e.g., between drugs and diseases) and
help prioritize potential biomarkers for complex conditions. The
Integrative Biomedical Knowledge Hub (iBKH) is one such
large-scale graph that consolidates diverse biomedical resources
into a unified hub, enabling discovery at scale [11]. Beyond
iBKH, large biomedical knowledge graphs such as SPOKE
further illustrate how graph integration accelerates research and
supports precision-medicine use cases [12]. Overall, these graphs
serve as foundational resources for data-driven and personalized
medicine. These knowledge graphs serve as foundational
resources for precision medicine, where treatment can be tailored
to the individual’s biological profile, improving outcomes and
minimizing side effects [19, 31].
TABLE I. SUMMARY OF RELATED RESEARCH ON BIOMEDICAL KGS AND QUESTION ANSWERING
Ref.
Year
Data/Graph
Method
Baselines
Key Metric
Limitation
[11]
2023
iBKH (18 biomedical
sources, Neo4j)
Integrative KG + Graph
learning; drug repurposing
case
Known drugs,
Alzheimer’s
study
Predictive performance
(drug repurposing)
Limited to Alzheimer’s case
study; scalability and updates not
detailed
[19]
2023
Disease KG (9,400
nodes, 45,000 relations
in Neo4j)
Cypher queries for disease–
drug–symptom reasoning
Cross-referential
disease DB
New relation inference;
complex query
answering
Limited to single domain; lacks
large-scale evaluation
[3]
2023
BKGs vs. GPT-3.5/4
Comparative QA study:
LLMs vs. KGs
GPT-3.5, GPT-4,
KG reasoning
Accuracy, Novelty,
Reasoning
GPT-4 inconsistent in
reasoning/citations; KG less fluent
but more reliable
[13]
2024
SPOKE KG +
LLaMA2, GPT-3.5,
GPT-4
KG-optimized retrieval for
LLMs (RAG)
Other retrieval
methods
Accuracy, token
reduction >50%
Focus on retrieval optimization,
not KG construction
[23]
2024
Benchmark KB datasets
Generate-then-retrieve
(LLM → logical form →
KB retrieval)
Prior KBQA
methods
Accuracy (highest to
date)
Risk of error in logical form
generation
[6]
2024
PrimeKG
LLM + KG hybrid, Cypher
query validation, RAG
GPT-4 Turbo,
LLaMA 3
Accuracy,
Explainability
Dependent on KG coverage;
computationally intensive
[22]
2025
Pan-cancer KG (PcQA
dataset)
KG-enhanced reasoning
(subgraph selection)
SOTA KGQA
methods
Outperformed SOTA on
PcQA
Limited to pan-cancer focus; no
fine-tuning explored
[26]
2025
Dynamic Medical KG +
Neo4j
LLM agents + multi-hop
reasoning
MEDQA,
MedMCQA
baselines
Accuracy,
Interpretability
High system complexity; requires
continuous updating
[24]
2025
SynthCypher dataset
(Neo4j + synthetic
queries)
LLM-supervised Cypher
generation and verification
SPIDER
benchmark
Cypher accuracy 40%
Synthetic dataset may not capture
all real-world cases
[29]
2025
UMLS KG + Neo4j
Hybrid GraphRAG
LLM-only QA
Accuracy, Citation
fidelity
More complex pipeline; relies on
external vector store
[30]
2025
Clinical data (10k
patients, 1,894 nodes,
Neo4j)
KG construction using
GPT-4 for entity/relation
extraction
Other KG
construction
methods
High F1-scores
Focus only on sepsis; limited
generalization
[20]
2025
Local KG from
biomedical documents
Multi-level summarization
over KG for QA
RAG baselines
QA accuracy, reasoning
Tested mainly on document QA;
scalability not proven
B. Neo4j Graph Database
To manage the complexity and large size of biomedical
knowledge graphs, specialized graph databases are needed.
Neo4j is one of the most popular graph databases designed to
store and query data structured as nodes (entities) and
relationships (edges), both of which can have descriptive
properties [32, 33]. It uses the property graph model, which
makes it easy to represent complex, connected biomedical data
such as drug-gene interactions or disease pathways. Neo4j’s
Cypher query language is especially advantageous because it
allows users to write expressive and efficient queries to explore
multi-step connections in the data [34].
Neo4j works well for biomedical data because it can quickly
run complicated queries over highly interconnected datasets. This
is important in biology and medicine, where relationships
between entities like proteins, diseases, and drugs are often
complex and layered. Studies have shown that Neo4j handles
large biomedical graphs efficiently, making it a favorite among
researchers and industry users alike [33, 35, 36]. Its indexing and
caching mechanisms also help speed up query processing and
data retrieval [37].
Moreover,
Neo4j
integrates
smoothly
with
many
programming languages and analytics tools, which makes it
easier to build interactive biomedical applications and clinical
decision support systems that can turn complex graph data into
useful insights [38, 39].
C. Large Language Models (LLMs) in Biomedical Question
Answering
Large Language Models (LLMs) are powerful AI systems
trained on vast amounts of text data. They learn the structure and
patterns of language, enabling them to understand questions,
generate responses, summarize information, and perform other
complex language tasks. Well-known models such as LLaMA
and GPT-3 have greatly advanced the field of natural language
processing by showing strong performance across many tasks
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
347
In biomedical research and clinical settings, LLMs help
translate natural language questions from doctors, researchers, or
patients into precise, structured queries that can be executed on
biomedical knowledge graphs and databases. This makes it easier
to retrieve detailed biomedical information like drug interactions,
gene-disease associations, and symptom descriptions [42, 43].
Despite their power, LLMs can sometimes generate incorrect
or fabricated responses, a phenomenon known as hallucination,
which poses risks in sensitive biomedical contexts. These
hallucinations occur because the models generate plausible
answers based on patterns learned from data rather than verified
knowledge. To mitigate this, researchers integrate LLMs with
biomedical knowledge graphs to ground answers in factual data,
significantly improving accuracy and reducing misinformation
[4]. Further improvements come from fine-tuning LLMs on
biomedical corpora and carefully engineering prompts, which
enhance their reliability and relevance in medical question
answering.
Additionally, combining LLMs with knowledge graphs and
reasoning techniques is an active area of research that promises
to increase the interpretability and trustworthiness of AI systems
in biomedicine. These advances are critical for developing tools
that assist clinical decision-making and accelerate biomedical
discoveries [43, 44].
IV. METHODS AND MATERIALS
This section describes the methodology used to build a
biomedical question-answer system. The proposed method
consists of three main stages; First, a biomedical knowledge
graph is constructed in the data ingestion phase, using structured
data sources (e.g., diseases, drugs, symptoms). Second, a
language model (LLaMA 3) interprets the user’s question
written in English in the user interaction phase and converts it
into a Cypher query. Third, a graphical user interface allows
users to type questions and view the results interactively.
A. System Architecture
The proposed framework is organized as a step-by-step
pipeline that integrates a local large language model (LLM) with
a biomedical knowledge graph stored in Neo4j. The overall
workflow is illustrated in Fig. 2. Each module performs a
specific function, and together they ensure that the system
delivers accurate, reliable, and explainable answers.
Fig. 2. Workflow of the LLaMA 3 and Neo4j-Based QA System
Step 1. User Input (Flask Web Interface): A user submits
a biomedical question in natural language through a Flask-based
web interface. The same interface will later display the answer,
the executed query, and a compact preview of the retrieved rows.
Step 2. LLM Processing and Initial Cypher: The text query
is forwarded to a local LLaMA 3 module, which interprets the
intent and drafts an initial Cypher pattern suitable for querying
the graph.
Step 3. Cypher Query Generation: The Cypher Query Gen
block receives the initial pattern from LLaMA 3, canonicalizes
and completes it (projection, DISTINCT, filters), and returns a
finalized Cypher query to the model.
Step 4. Query execution on Neo4j: LLaMA 3 passes the
finalized query to the Query execution component (inside the
’Answer modules’ box), which runs it against the Neo4j
Knowledge Graph. KG supplies knowledge graph data (e.g., | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
348
drugs, diseases, symptoms) and execution returns structured data
(tuples) relevant to the question.
Step 5. Answer Synthesis: The structured tuples flow to
Answer Synthesis, which aggregates and formats them into a
concise raw answer. This raw answer is sent back to LLaMA 3
to optionally refine the phrasing while preserving the retrieved
facts.
Step 6. Result Presentation: LLaMA 3 produces the final
answer, which the interface displays together with the executed
Cypher query and an optional preview of the returned rows,
improving transparency and trust.
The pipeline couples LLM-based language understanding
(LLaMA 3) with a schema-grounded Neo4j knowledge graph.
The Cypher Query Gen refines the query formulation, Query
Execution retrieves evidence and Answer Synthesis converts
structured results into readable outputs that produce answers that
are accurate, interpretable, and easy to audit directly from the
displayed query and evidence.
B. Dataset and Knowledge Graph Construction
1)
Dataset
The integrated Biomedical Knowledge Hub (iBKH), a large
biomedical knowledge base, forms the first level of the system
and integrates information from various curated high-quality
biomedical databases. This implies that the data set includes
various types of entities, such as diseases, symptoms, drugs,
biological pathways, etc. This study used the representative
subset of the iBKH dataset, which contained 65828 biomedical
entities. These entities are semantically interconnected through a
total of 3004166 relationships, thus creating a rich knowledge
graph. The iBKH dataset was originally introduced in [11], and it
is freely available at (https://github.com/wcm-wanglab/iBKH).
This dataset is the core semantic foundation upon which this
study is built. The knowledge graph is populated from multiple
tabular sources (CSV files), each listing entities or relationships.
The main input files and their contents are as follows:
• Disease
vocabulary(disease_vocab.csv):
Contains
columns such as primary (a unique disease ID), name,
do_id (Disease Ontology ID), kegg_id, and umls_cui
(UMLS Concept ID). Each row represents a disease node
with external identifiers.
• Drug vocabulary (drug_vocab.csv): Includes primary
(unique drug ID), name, drugbank_id, kegg_id,
pharmgkb_id, umls_cui, mesh_id, iDISK_id and CID
(PubChem ID). Each row defines a drug node with
standard database identifiers.
• Symptom vocabulary (symptom_vocab.csv): Contains
primary (unique symptom ID), name, mesh_id, umls_cui
and iDISK_id. Each row defines a symptom node.
• Side
effect
vocabulary
(side_effect_vocab.csv):
Includes primary (unique side-effect ID) and name. Each
row represents a side-effect node (with UMLS ID when
available).
• Pathway vocabulary (pathway_vocab.csv): Contains
primary (unique pathway ID), name, reactome_id, go_id,
and kegg_id. Each row defines a biological pathway
node.
Relationship files (each row typically contains two entity IDs
and one or more boolean flags or codes) include:
• Disease–Symptom links (Di_Sy_res.csv): Rows include
Disease and Symptom IDs, a presence flag (1 or 0) and a
data source. If Present = 1, a HAS_SYMPTOM edge is
created from the disease to the symptom, with properties
for presence and source.
• Disease–Disease links (di_di_res.csv): Rows include
Disease_1 and Disease_2 IDs with binary flags for is_a
and Resemble. If is_a = 1, an (IS_A) edge is created
(Disease_1 → Disease_2); if Resemble = 1, a
RESEMBLES edge is created. The source field is used
for provenance.
• Drug–Disease links (D_Di_res.csv): Includes Drug and
Disease IDs with several binary flags. If a flag equals 1,
a corresponding edge is created:
o
TREATS (Treats = 1)
o
PALLIATES (Palliates = 1)
o
ASSOCIATED_WITH (Associate = 1)
o
ALLEVIATES_REDUCES (alleviates = 1)
o
TREATMENT_THERAPY (treatment/therapy =
1)
o
INHIBITS_CELL_GROWTH
(inhibits
cell
growth = 1)
o
HAS_BIOMARKER (biomarkers = 1)
o
PREVENTS_SUPPRESSES
(prevents/suppresses = 1)
o
ROLE_IN_PATHOGENESIS (role in disease
pathogenesis = 1)
• Drug–SideEffect links (D_SE_res.csv): Contains Drug
and SideEffect IDs with a Source column. Each row
creates a CAUSES edge from the drug to the side effect,
with source as an edge property.
• Drug–Drug interactions (D_D_res.csv): Rows include
Drug_1 and Drug_2 IDs with flags for Interaction and
Resemble. If Interaction = 1, an INTERACTS_WITH
edge is created (bidirectional). If Resemble = 1, a
RESEMBLES edge is added.
• Drug–Pathway links (D_Pwy_res.csv): Includes Drug
ID and Pathway ID. Each row generates an
ASSOCIATED_WITH edge from the drug to the
pathway.
• Disease–Pathway links (Di_Pwy_res.csv): Contains
Disease ID and Pathway ID. Each row creates an
ASSOCIATED_WITH edge from the disease to the
pathway.
2)
Data Upload Performance
The time required to upload different types of entities and
relationships into the Neo4j biomedical knowledge graph,
measured in seconds. These measurements reflect both the size
and complexity of the data being processed.
As shown in Table II, the longest upload time is for Drug-
Drug Relationships, which takes approximately 190 seconds due
to the large number of edges (over 3 million). Following this,
Disease-Disease and Drug-Disease Relationships also require
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
349
entities such as Diseases and Drugs are uploaded much faster,
generally under 2 seconds.
TABLE II. DATA UPLOAD TIMES FOR DIFFERENT ENTITY AND RELATIONSHIP
TYPES IN NEO4J
Entity / Relationship Type
Upload Time (seconds)
Disease
0.81
Drugs
1.08
Symptoms
0.06
Side Effects
0.14
Pathways
0.08
Disease-Disease Relationships
30.97
Drug-Disease Relationships
30.28
Drug-SideEffect Relationships
5.24
Drug-Drug Relationships
190.09
Drug-Pathway Relationships
0.14
Disease-Pathway Relationships
0.06
Disease-Symptom Relationships
0.12
Fig. 3, presents a vertical bar chart that visually compares
these upload times across the different entity and relationship
types. The chart clearly shows the significant difference in upload
duration between nodes and edges, emphasizing the higher cost
of ingesting complex relationships in the graph.
3)
Experimental Environment
In this study, the proposed biomedical question answering
system was evaluated using a locally hosted environment. All
experiments were conducted on a Windows 11 Pro (64-bit)
system equipped with an Intel Core i5-10500H processor running
at 2.50 GHz (12 logical CPUs), 24 GB of RAM, and an NVIDIA
GeForce GTX 1650 GPU with Max-Q Design. The Neo4j graph
database (v4.4.5) was managed through Neo4j Desktop (v1.6.2),
and the LLaMA 3B language model was executed locally using
optimized configurations suitable for this hardware setup.
Each Cypher query generated by the system was executed
multiple times to calculate an average response time, ensuring
consistency across varying levels of question difficulty. The
knowledge graph was constructed using the iBKH dataset, and
data loading and system performance were carefully monitored
to maintain stability during testing. This experimental setup
provides a reliable and reproducible environment for
benchmarking the effectiveness and responsiveness of the hybrid
QA system.
Fig. 3. Upload times for various biomedical entities and relationships in Neo4j.
We run LLaMA 3 locally (not via cloud APIs) to satisfy
biomedical privacy/governance (no data leaves the host) and to
maximize reproducibility (fixed GGUF checkpoint, pinned
llama.cpp commit, controlled seeds settings, constant hardware).
Local execution yields predictable cost availability and stable
latency (no network jitter) and lets us enforce executable Cypher
grounding with per edge provenance and timestamp aware
execution.
4)
Knowledge Graph(KG) Construction
The Neo4j graph database was used as the backend to store
and query the KG, and it is a graph database designed for highly
connected data. Before loading data, uniqueness constraints were
created on the primary property for each node label (Disease,
Drug, Symptom, SideE ffect, Pathway). This enforces that each
primary ID appears only once, preventing duplicate entities. For
efficient lookups in queries, a search index was created based on
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
350
documentation, indexes “enable quicker and more efficient
pattern matching” by allowing the query planner to rapidly locate
nodes by label and property.
With the schema in place, data was imported using Cypher’s
LOAD CSV commands. For each vocabulary file, rows with
nonnull primary IDs were processed: the code used MERGE to
create (or match existing) nodes with the given label and primary
property, and then SET the remaining attributes from the CSV
columns. For example, in importing disease_vocab.csv, each row
produced a node (:Disease primary: <id>) with properties name,
do_id, kegg_id, and umls_cui set from the row (empty strings
were turned into null). Similarly, drug_vocab.csv rows produced
drug nodes with properties for DrugBank, KEGG, PharmGKB
IDs, etc. This approach follows best practice: using MERGE on
the unique ID ensures that nodes are not duplicated during
multiple passes.
After all nodes were created, the relationship CSVs were
loaded. Each row in those files was matched to the corresponding
source and target nodes by their primary IDs, and conditional
logic was used to create edges. For example, the disease-
symptom file (Di_Sy_res.csv) was processed by matching a
disease node and a symptom node for each row, then executing
MERGE (d)-[r:HAS_SYMPTOM]-(s) if the present column is
nonzero; the edge was given a present property and a source
property from the CSV. The disease-disease file (di_di_res.csv)
was processed by matching disease nodes d1 and d2:If is_a = 1,
a (:Disease)-[:IS_A]->(:Disease) edge was merged; if Resemble
= 1, a (:Disease)-[:RESEMBLES]->(:Disease) edge was merged.
Drug-disease relationships were handled similarly: the script
matched a Drug node and a Disease node for each row of
D_Di_res.csv, then for each flag that was 1, it merged the
corresponding edge label (such as TREATS, PALLIATES,
ASSOCIATED_WITH, etc.) from the drug to the disease. Drug–
side-effect rows produced (:Drug)-[:CAUSES]->(:SideEffect)
edges with the source noted, and drug–drug rows produced either
INTERACTS_WITH or RESEMBLES edges between matched
drug pairs. Finally, the drug-pathway and disease-pathway files
each produced:ASSOCIATED_WITH edges linking drugs or
diseases to pathway nodes.
In this graph model, most relationships are directional (for
example, a drug TREATS a disease; a disease does not TREAT
a drug). This follows common practice in biomedical KGs. The
same relations (such as RESEMBLES or INTERACTS_WITH)
are inherently symmetric, but were stored as two directed edges
or one undirected edge depending on implementation. All
relationship creation steps used Cypher’s MERGE so that
repeated loads or out-of-order imports did not create duplicate
edges.
This study used a static KG snapshot for reproducibility, but
the system also supports incremental updates through the Neo4j
MERGE and batch import functions. A summary of the different
types of nodes and relationships is provided, together with their
counts, descriptions, and examples in Table III.
TABLE III. DETAILED SUMMARY OF KNOWLEDGE GRAPH COMPONENTS IN IBKH SUBSET
Component Type
Entity/Relationship
Count
Description
Examples
Disease
19,236
Medical conditions and disorders
Diabetes, Hypertension, Cancer
Drug
37,997
Pharmaceutical compounds
Aspirin, Insulin, Amoxicillin
Nodes
Symptom
1,361
Clinical signs and symptoms
Headache, Fever, Nausea
SideEffect
4,251
Negative outcomes of drugs
Nausea, Drowsiness
Pathway
2,983
Biological processes and pathways
Apoptosis, Glycolysis
ASSOCIATED_WITH
101,534
General associations (disease–
pathway, drug–pathway, etc.)
Diabetes ASSOCIATED_WITH Pathway
CAUSES
145,321
Drug-SideEffect relationships
Aspirin CAUSES Gastric_Bleeding
HAS_SYMPTOM
3,357
Disease-symptom links
COVID-19 HAS_SYMPTOM Fever
HAS_BIOMARKER
412
Biomarkers linked to disease
PSA HAS_BIOMARKER Prostate_Cancer
Relationships
INHIBITS_CELL_GROWTH
1,508
Drugs inhibiting cell growth
Chemo INHIBITS_CELL_GROWTH Tumor
INTERACTS_WITH
2,682,142
Drug-drug interactions
Aspirin INTERACTS_WITH Warfarin
IS_A
10,529
Subtype hierarchy
Flu IS_A Viral_Infection
PALLIATES
388
Drug palliates disease
Morphine PALLIATES Cancer
PREVENTS_SUPPRESSES
859
Preventive links
Vaccine PREVENTS_SUPPRESSES Measles
RESEMBLES
7,000
Similarity relationships
DrugA RESEMBLES DrugB
TREATMENT_THERAPY
44,852
Therapy relationships
Radiotherapy TREATMENT_THERAPY Tumor
TREATS
5,491
Drug-disease links
Insulin TREATS Diabetes
ALLEVIATES_REDUCES
∼180,000
Symptom relief
Paracetamol ALLEVIATES_REDUCES Fever
Total
Nodes
65,828
Total biomedical entities
—
Relationships
3,004,166
Total knowledge graph links
—
C. Natural Language to Cypher Query Translation
A key feature of the system is its ability to accept questions
written in plain English and automatically generate the
corresponding Cypher queries. This is accomplished using
Meta’s LLaMA 3 large language model, which runs entirely on
a local machine through the open-source llama.cpp framework.
Running the model locally ensures low-latency execution and
keeps sensitive queries within the user’s environment.
To generate a Cypher query, LLaMA 3 is prompted with
examples of natural language questions along with their correct
Cypher translations. The prompt also includes instructions on
how to navigate the structure of the graph schema. When a user
enters a question (e.g., ’What are the symptoms of Alzheimer’s
disease?’), the system inserts it into the prompt and asks LLaMA
3 to produce a corresponding query. For example, the model may
return:
MATCH (d:Disease)-[:HAS_SYMPTOM]->(s:Symptom)
WHERE toLower(d.name) CONTAINS "alzheimer"
RETURN s.name | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
351
This query searches for a disease node whose name contains
’alzheimer’ and follows HAS_SYMPTOM edges to list related
symptom names. The system then executes this cypher to retrieve
answers. The prompts (such as few-shot examples and schema
hints) were carefully designed to help LLaMA 3 generate correct
Cypher queries. The model learns how to use the graph’s labels
and relationships properly. For example, if a user asks, ’Which
drugs treat diabetes?’, LLaMA might respond with a suitable
Cypher query:
MATCH (d:Drug)-[:TREATS]->(di:Disease)
WHERE toLower(di.name) CONTAINS "diabetes"
RETURN d.name
This queries for drug nodes that have a TREATS edge to a
diabetes disease node. By leveraging LLaMA 3 in this way, our
system can flexibly handle many phrasing variations without
manual mapping rules.
D. Model Configuration & Decoding
We run a local LLaMA 3.2-3B model in GGUF format (llama-
3.2-3b-instruct-q4_k_m.gguf) via llama.cpp, as shown in Table
IV.
TABLE IV. MODEL RUNTIME AND DECODING SETTINGS
Runtime settings
Decoding settings
n_ctx = 1024
temperature = 0.2
n_threads = 12
top_p = 0.95
n_gpu_layers = 33
top_k = 40
n_batch = 512
repeat_penalty = 1.1
max_tokens = 80
seed = 42
E. Graph Subset and Versioning
We use an iBKH derived subgraph (≈65.8k nodes; ≈3.0M
edges) spanning DRUG, DISEASE, SYMPTOM, PATHWAY. IDs are
normalized to CURIEs and duplicates collapsed across
UMLS/DrugBank/DisGeNET/SIDER/KEGG. Each edge stores
provenance/licensing metadata (source, source_version, license,
retrieved_at,
evidence_pmids/urls)
and,
when
available,
temporal fields (valid_from, valid_to). We report coverage as
the
percentage
of
evaluated
questions
whose
gold
entities/relations are present.
F. Query Execution and Reliability
After a Cypher query is generated, it is executed on the Neo4j
database through the official Neo4j Python driver, which
manages the secure connection and returns the results. Instead of
restricting the output with a fixed LIMIT (e.g., LIMIT 5), the
system retrieves candidate results and evaluates them using
standardized retrieval metrics such as Hits@1, Hits@5, and
Hits@10. This approach ensures that the system remains
responsive while providing a fair assessment of ranking quality
across different cutoff levels, rather than depending solely on a
fixed number of returned items. Neo4j’s indexing on key node
properties, such as name and primary identifiers, also helps speed
up lookups as the knowledge graph grows. In cases where the
language model generates an incomplete or incorrect query, such
as referencing nodes or relationships that do not exist, the system
catches the error and either retries with a simpler prompt or
informs the user. Together, these steps make sure that queries run
quickly, return valid results, and keep the overall experience
smooth and reliable for biomedical question-answering.
G. User Interface for Query Transparency
The system includes a lightweight, cross-platform graphical
user interface (GUI) implemented as a web application using the
Flask framework in Python, with HTML and Bootstrap for
interactive visualization. The interface is designed to make the
question-answering process transparent and accessible to users
without technical expertise. It consists of three main panels:
1. Input Panel: Where the user can enter a biomedical
question in natural language.
2. Query Panel: Which displays the Cypher query generated
by the language model, allowing users to verify how their
question was interpreted.
3. Results Panel: Which presents the retrieved answers in a
clear, readable format, accompanied by a brief natural
language explanation generated by the system.
By showing both the query and the answer, the GUI promotes
user trust and enables validation of the system’s reasoning
process. The interface is lightweight enough to run smoothly on
standard desktop machines without additional dependencies,
making it practical for local deployments in clinical or research
settings. Fig. 4 illustrates the overall layout.
V. EXPERIMENTAL RESULTS
In this section, the proposed system is evaluated based on its
ability to translate natural-language biomedical questions into
executable Cypher queries over the iBKH knowledge graph. The
assessment is conducted end-to-end and focuses on three main
aspects: (i) the accuracy of query generation, measured by the
proportion of correctly produced Cypher queries; (ii) system
efficiency, quantified through total response time from question
submission to final answer delivery, covering both query
generation and execution; and (iii) the quality of retrieved
answers, evaluated using standardized information retrieval
metrics including Precision@k, Recall@k, F1@k, Hits@k, and
Mean Reciprocal Rank (MRR). For clarity, all these metrics are
formally defined in the following subsections, with their
corresponding equations, and will be used consistently
throughout the results section. Together, these dimensions
provide a comprehensive view of both correctness and efficiency
across simple, medium, and complex biomedical queries.
A. Description of the Experimental Data
To evaluate the proposed system, this work used a carefully
selected set of biomedical questions designed to test how well the
system understands natural language and converts it into Cypher
queries for a biomedical knowledge graph.
To evaluate the system, a benchmark of 60 biomedical
questions was constructed by the authors, guided by the schema
and relations in iBKH. The questions were grouped into simple,
medium, and complex levels to assess performance across
different reasoning requirements. Gold-standard answers were
manually prepared for each question to enable quantitative
evaluation using Exact Match, Precision@k, Recall@k, F1@k,
Hits@k, and MRR. The complete set of 60 questions is available
at this link https://drive.google.com/drive/my-drive. | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
352
The dataset contains 60 questions divided into three difficulty
levels based on how complex the language is and how deep the
biomedical reasoning needs to be:
• Level 1: 25 simple questions focusing mostly on easy-to-
understand facts, such as symptoms of certain diseases or
drugs used to treat specific conditions.
• Level 2: 20 medium-level questions that involve more
detailed relationships, such as drug interactions and
SideEffect.
• Level 3: 15 hard questions requiring multi-step reasoning
across multiple biomedical concepts or biological
pathways, similar to the complexity found in real clinical
cases.
The set of evaluation questions was designed to span a wide
range of common biomedical topics and to reflect clinically
relevant query types reported in prior literature. Each question is
paired with its corresponding gold standard cypher query and
categorized by difficulty level, as summarized in Table V, where
three illustrative examples are shown. The questions were derived
from publicly available biomedical QA benchmarks and adapted
from established knowledge bases (e.g., iBKH schema relations),
ensuring both coverage and diversity across diseases, drugs,
symptoms, and pathways.
TABLE V. QUESTION DIFFICULTY LEVELS AND SAMPLE CYPHER QUERIES
Level
Definition
Example
Cypher Query
1
Single-hop question using
one relationship
What are the symptoms of
Alzheimer?
MATCH (d:Disease)-[:HAS_SYMPTOM]->(s:Symptom)
WHERE toLower(d.name) CONTAINS 'alzheimer'
RETURN s.name AS symptom
2
Questions requiring one
relationship
What are the side effects of
drugs used to treat asthma?
WITH toLower('asthma') AS disease_name
MATCH (d:Disease)<-[:TREATS]-(dr:Drug)
WHERE toLower(d.name) CONTAINS disease_name
MATCH (dr:Drug)-[:CAUSES]->(se:SideEffect)
RETURN DISTINCT se.name AS side_effect, dr.name AS drug
3
Questions requiring two or
more relationships
What cholesterol medications
cause side effects, and what are
some of those effects?
WITH toLower('cholesterol') AS disease_name
MATCH (d:Disease)<-[:TREATS]-(dr:Drug)
WHERE toLower(d.name) CONTAINS disease_name
MATCH (dr:Drug)-[:CAUSES]->(se:SideEffect)
RETURN DISTINCT dr.name AS drug, se.name AS side_effect
Fig. 4. Graphical User Interface of the Biomedical Knowledge Explorer System
B. Quantitative Evaluation
The performance of the proposed system is evaluated on the
iBKH knowledge graph using Exact Match, Precision@k,
Recall@k, F1@k, Hits@k, MRR, and total response time across
simple, medium, and complex biomedical queries. These metrics
were measured for each difficulty level (simple, medium, and
complex) to see how performance changes as questions become
more challenging.
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
353
Difficulty Level
Total Questions
Correct Queries
Cypher Exact Match (EM) (%)
Simple
25
24
96%
Medium
20
19
95%
Complex
15
13
86.7%
avg
92.6%
To better understand the quality of the responses returned by
the system, this work examined real examples. One of the test
questions was "What drugs are used to treat breast cancer?" As
shown in Fig. 4, the system was able to understand the question,
generate the correct Cypher query using the TREATS
relationship, and retrieve the right information from the
biomedical knowledge graph. It returned a list of five drugs,
including Leuprolide, Goserelin, and Trastuzumab, which are
known treatments for breast cancer. This result shows that the
system is able to connect the question to the right part of the graph
and provide accurate and helpful answers, even for medical
questions that involve specific treatments.
1)
Exact Match of Query Generation
As shown in Table VI, how often the system generated the
correct query for each difficulty level. Here, accuracy is defined
as the percentage of questions for which the system’s generated
Cypher query matched the expected query. It is calculated using
the Eq. (1)
Exact Match (%) = Correct Queries
Total Questions × 100
(1)
These findings highlight the purpose of this experiment to test
whether the framework can reliably map natural language to
Cypher across varying levels of complexity. The graceful drop
from 96% on simple to 86.7% on complex queries indicates that
the system is robust for straightforward questions but still
challenged by multi-hop reasoning. This points to clear
opportunities for improvement, such as synonym expansion,
constrained decoding, or enhanced error handling for multi-step
queries.
2)
Latency
Table VII reports the average latency per difficulty level and
decomposes it into query generation and Neo4j execution. The
total response time is computed as in Eq. (2). Execution time is
effectively constant across all levels ( ≈0.04–0.05 s), so
variation in total latency is dominated by query generation. As
difficulty increases, the mean total time rises from 5.12 s
(simple) to 5.75 s (medium) and 7.35 s (complex). Dispersion
(Std. Dev.) grows with task complexity 0.72 s (simple), 0.32 s
(medium), 2.09 s (complex) reflecting more variable planning
and reasoning needed to assemble correct Cypher for harder
questions. Pooled over all questions, the overall mean is 6.07 s
with an overall SD of 1.38 s, keeping latencies within single-digit
seconds and practical for interactive, real-world use.
𝑇total = 𝑇gen + 𝑇exec
(2)
Standard Deviation (SD). Unless otherwise stated, SD is the
unbiased sample standard deviation computed over per-question
total times within each difficulty group 𝐺 with 𝑛𝐺 questions:
𝑇‾𝐺= 1
𝑛𝐺
∑𝑇total
(𝑖)
𝑛𝐺
𝑖=1
,
𝑠𝐺= √
1
𝑛𝐺−1 ∑
𝑛𝐺
𝑖=1
(𝑇total
(𝑖) −𝑇‾𝐺)
2 .
(3)
(4)
Overall values are computed by pooling all questions across
levels, with 𝑁= ∑𝑛𝐺
𝐺
:
𝑇‾overall = 1
𝑁∑𝑇total
(𝑖)
𝑁
𝑖=1
,
𝑠overall = √
1
𝑁−1 ∑
𝑁
𝑖=1
(𝑇total
(𝑖) −𝑇‾overall)
2 .
(5)
(6)
TABLE VII. AVERAGE LATENCY (S) BY DIFFICULTY LEVEL (SD = STANDARD
DEVIATION)
Difficulty
Level
Query
Generation (s)
Query
Execution (s)
Total
Time (s)
Std.
Dev. (s)
Simple
5.09
0.03
5.12
0.72
Medium
5.69
0.01
5.75
0.32
Complex
6.94
0.4
7.35
2.09
Overall
5.9
0.15
6.07
1.38
3)
Answer-Level Evaluation Metrics
As shown in Table VIII, the quality of the returned items is
evaluated using five standardized metrics: Precision@k,
Recall@k, F1@k, Hits@k, and MRR. Precision@k measures the
proportion of correct answers among the top-𝑘 retrieved items
Eq. (7), while Recall@k quantifies the fraction of gold-standard
answers covered within the top-𝑘 results Eq. (8). F1@k combines
both aspects through the harmonic mean Eq. (9). Hits@k reports
whether at least one correct answer appears in the top-𝑘 Eq.
(10, 11), and MRR captures how early the first correct answer is
retrieved in the ranking Eq. (12). Together, these metrics provide
a comprehensive view of both the correctness and completeness
of retrieval, as well as the ranking quality across simple, medium
and complex queries.
Precision@𝑘= |{ relevant ∩retrieved@𝑘 }|
𝑘
(7)
Precision@k measures the fraction of the top-𝑘 retrieved
items that are correct (i.e., appear in the gold set). For example, | {
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
354
P@1 refers to the accuracy of the very first retrieved item, P@5
evaluates correctness within the top three results, and P@10
considers the top ten. Higher values indicate that relevant items
tend to appear early in the ranked list.
TABLE VIII. EVALUATION METRICS ACROSS LEVELS
Simple
Medium
Complex
Overall
P@1
100
95
86.67
93.8
P@5
100
95
93.33
96.11
P@10
100
95
88
94.33
R@1
88
90
86.67
88.22
R@5
90.65
89.67
86.67
88.9
R@10
89.60
89
83.33
87.31
F1@1
88
90
86.67
88.22
F1@5
93.20
91.25
88.67
91.03
F1@10
93.28
91.39
85.24
89.97
Hits@k
100
95
93
96.1
MRR
100
95
88
94.4
Recall@𝑘=
|{ relevant∩retrieved@𝑘 }|
|{ relevant }|
(8)
Recall@k measures the proportion of all relevant items (in the
gold set) that are successfully retrieved within the top- 𝑘
positions. For instance, R@5 indicates how many of the expected
answers are covered by the top three results. This metric is
particularly important when the gold answer set is larger than 𝑘.
F1@𝑘= 2 ⋅Precision@𝑘⋅Recall@𝑘
Precision@𝑘+ Recall@𝑘
(9)
F1@k is the harmonic mean of Precision@k and Recall@k.
It balances the trade-off between retrieving only relevant items
(precision) and covering as many relevant items as possible
(recall). For example, F1@10 reflects the combined quality of the
system when retrieving the top ten results.
Hits@𝑘= {1
if { relevant ∩retrieved@𝑘 } ≠⌀
0
otherwise
(10)
Hits@k is a binary measure at the query level: it equals 1 if at
least one correct answer is present among the top-𝑘 retrieved
items, and 0 otherwise. For example, Hits@5 reports the
percentage of queries where the system was able to “hit” at least
one correct answer in the top five results.
Hits@𝑘= 1
|𝑄| ∑𝟏
𝑞∈𝑄
({ relevant𝑞
∩retrieved𝑞@𝑘 } ≠⌀)
(11)
When averaged across a set of queries 𝑄, Hits@k gives the
proportion of queries for which at least one relevant item is
returned in the top-𝑘 results. This measure is less sensitive to
ranking quality but emphasizes coverage across queries.
MRR = 1
|𝑄| ∑
1
rank𝑞
𝑞∈𝑄
(12)
Mean Reciprocal Rank (MRR) averages the reciprocal of the
rank of the first correct answer for each query 𝑞∈𝑄. For
example, if the first correct answer appears in the top position,
the reciprocal rank is 1/1 = 1.0; if it appears at rank 5, the
contribution is 1/5 = 0.2. MRR therefore rewards systems that
retrieve correct answers as early as possible.
According to the results in Table VIII, this work reports
standardized metrics, including Precision, Recall, F1, Hits@k,
and MRR at different cutoffs (𝑘= 1,5,10). Precision@k captures
the proportion of retrieved items among the top-𝑘 that are correct,
while Recall@k measures the coverage of gold-standard answers
within the same cutoff. F1@k balances both aspects. Hits@k
reflects whether at least one correct answer appears in the top-𝑘,
and MRR evaluates how early the first correct answer is retrieved.
For simple questions ( 𝑁= 25 ), the performance is
consistently near perfect, with P @ 10 = 100%, R @ 10 (89. 6%)
and F1 @ 10(93. 3%), along with Hits @ 10 = 100% and MRR =
100%. For medium questions (𝑁= 20), P @ 10 = 95%, R @ 10
(89%) and F1 @ 10 (91. 4%), with a strong ranking quality
reflected in Hits@10 = 95% and MRR = 95%. For complex
queries (𝑁= 15), the performance remains robust but slightly
lower, with P @ 10 (93. 3%), R @ 10 (88%) and F1 @ 10 (90.
2%), alongside Hits @ 10 (93. 3%) and MRR 93. 3%.
In general, at all levels, the system achieves P @ 10 (96. 1%),
R @ 10 (88. 9%) and F1 @ 10 (91. 6%), with Hits @ 10 (96. 1%)
and MRR 96. 1%. These results indicate that the system not only
retrieves nearly all expected answers but also ranks them highly,
ensuring both completeness and correctness. This level of
reliability is particularly valuable in biomedical applications
where precision and trustworthiness are critical.
The main purpose of this experiment was to assess not only
whether queries execute, but whether the returned results are both
correct and complete. The precision consistently above 95%
confirms that almost all the items retrieved are clinically valid,
while Hits@5 near 95% shows that the system usually returns
close to the expected five answers per question. Together, these
metrics demonstrate that knowledge graph grounding effectively
minimizes hallucinations and ensures trustworthy biomedical
output.
We evaluated performance differences across difficulty levels
using a two-sample t-test, which revealed statistically significant
differences. Error analysis indicates that failures in complex
queries are mainly due to missing relation hops, whereas errors
in medium-level queries are mostly caused by syntax
mismatches. These findings highlight the challenges associated
with query complexity and provide insights for targeted
improvements.
C. Qualitative Evaluation
In addition to quantitative metrics, the system’s outputs were
evaluated for contextual accuracy and alignment with the
structure of the knowledge graph. Two plain English questions
were selected and for each, the generated Cypher query, the
Neo4j output, and the visual graph representation were reviewed
to verify that the answers reflected valid relationships in the
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
355
For the question ’What are the symptoms of brain cancer?’
The system generated a Cypher query that correctly followed the
HAS_SYMPTOM relationship from disease nodes to symptoms
nodes, filtering by the specified disease name. The results
retrieved included terms such as aphasia (Broca, Wernicke),
anomia, agraphia, agnosia, amnesia (including retrograde
amnesia), olfaction disorders, and apnea symptoms consistent
with established neurological manifestations of brain tumors. In
Neo4j, the data formed a clear center-and-spoke pattern, with
brain cancer at the center and its associated nodes of symptoms
radiating outward, as shown in Fig. 5.
Cypher:
WITH toLower("brain cancer") AS disease_name
MATCH (d:Disease)-[:HAS_SYMPTOM]->(s:Symptom)
WHERE toLower(d.name) CONTAINS disease_name
RETURN DISTINCT s,d
LIMIT 10;
Fig. 5. Graphical user interface displaying Cypher query and results for breast
cancer treatment
A second query, What are the side effects of insulin?,
produced a Cypher statement starting from the drug node for
insulin and traversing the CAUSES relationship to the connected
SideEffect nodes. The resulting list included dizziness, diarrhea,
cough, back pain, weakness, rash/dermatitis, and hypersensitivity
side effects well documented in insulin safety profiles. In the
visual representation in Fig. 6, insulin appeared centrally
connected to these side effect nodes, further strengthening the
correctness of the relationship mapping of the system.
Cypher:
WITH toLower("insulin") AS drug_name
MATCH (d:Drug)-[:CAUSES]->(se:SideEffect)
WHERE toLower(d.name) CONTAINS drug_name
RETURN se,d
LIMIT 20;
Fig. 6. Knowledge Graph Visualization of Insulin and Its Related Side Effects
These examples demonstrate the system’s ability to interpret
plain English biomedical questions, generate correct Cypher
queries, and return results that are clinically plausible, easy to
interpret, and directly traceable to specific graph relationships.
This supports both the accuracy and the transparency of the
proposed approach.
To illustrate how biomedical questions can be translated into
graph queries, consider the natural language question: ’What are
the side effects of drugs that treat epilepsy?’. The following
Cypher query demonstrates how this question is mapped in the
knowledge graph, where diseases are connected to drugs through
the TREATS relation and drugs are connected to side effects
through the CAUSES relation.
Cypher:
MATCH (d:Disease)<-[:TREATS]-(dr:Drug)
WHERE toLower(d.name) CONTAINS "epilepsy"
MATCH (dr:Drug)-[:CAUSES]->(se:SideEffect)
RETURN DISTINCT se,d,dr
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Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
356
Fig. 7. Side effects of drugs that treat epilepsy
Executing this query returns drugs associated with epilepsy
and their corresponding side effects. For example, the query
identifies Pregabalin as a treatment for epilepsy and retrieves
multiple side effects such as anxiety, arrhythmia, gastrointestinal
pain, and others. The visualization of the graph in Fig. 7,
highlights this pattern, where the drug node is linked to epilepsy
via TREATS and to several side effect nodes via CAUSES,
providing an interpretable biomedical knowledge structure.
VI. DISCUSSION
Under
a
unified
evaluation
protocol
reporting
P@k/R@k/F1@k, Hits@k, and MRR at 𝑘∈{1,5,10} , the
pipeline exhibits consistent end-to-end behavior across the three
difficulty tiers. The 𝑘-ablation shows strong early precision (high
P@1 and elevated MRR), while recall increases with larger 𝑘,
indicating that correct answers typically surface near the top yet
persist deeper in the ranked slate. At the query-generation level,
exact-match (EM) is high for simple questions (e.g., ∼96%) and
lower for complex, multi-hop questions (e.g., ∼86.7%), which
aligns with increased compositionality. End-to-end latency
(mean total ≈6.07 s from question to final answer) remains within
interactive bounds on our local setup. Practically, a fully offline
deployment avoids internet connectivity, API keys, and external
data
transfer,
strengthening
privacy,
compliance,
and
reproducibility in biomedical contexts.
Several challenges qualify these findings. First, the
comparability challenge: to our knowledge, no prior study
evaluates iBKH using the same metric suite, making cross-paper
numeric comparisons not “apples to apples.” We therefore
interpret the results as controlled evidence about this pipeline
under a single, consistent protocol rather than as a cross-study
leaderboard. Beyond comparability, performance may vary with
other biomedical knowledge graphs; the current iBKH snapshot
is static, limiting real-time updates; and scaling to larger or
dynamically refreshed graphs can introduce latency and
consistency trade-offs.
Error analysis shows that residual failures concentrate in
complex, multi-hop queries where missing relation hops or brittle
name-based matching (synonyms, abbreviations, homonyms)
lead to partial answers. These observations motivate concept-
level normalization via biomedical identifiers (e.g., UMLS,
SNOMED, RxNorm) with synonym expansion, as well as
schema-constrained query generation and path-guided decoding
to better satisfy multi-hop constraints. Finally, correctness is
assessed primarily at the answer level (Hits@k, MRR,
precision/recall) and does not yet include full semantic-
equivalence checks across alternative Cypher queries, which may
overlook cases where different queries yield the same correct
results. Complementing template EM with result-set equivalence
checks, expanding the metric suite (e.g., nDCG/MAP) with per-
question 95% bootstrap confidence intervals, and supporting
incremental graph updates with distributed storage are promising
steps to enhance robustness, scalability, and external validity.
VII. CONCLUSION
This study introduced a hybrid biomedical question
answering framework that couples the LLaMA-3B language
model with a Neo4j-based iBKH knowledge graph to enable the
automatic generation of executable Cypher queries and to deliver
transparent, evidence-grounded answers through justification
subgraphs. Evaluation in simple, medium, and complex queries
demonstrated consistently high performance, with strong
precision, recall, F1, Hits@k, and MRR values, while
maintaining low latency suitable for interactive biomedical
applications. Beyond quantitative performance, the proposed
system provides a reproducible and privacy-preserving solution
by operating fully offline, a property of particular importance in
sensitive clinical and research contexts. However, certain
limitations remain. The reliance on a static iBKH snapshot
constrains coverage and adaptability, recall is lower for complex
multi-hop reasoning, and the absence of canonical entity
normalization (e.g., UMLS, SNOMED, RxNorm) may reduce
semantic robustness. Future research will therefore focus on
integrating standardized biomedical entity normalization,
enabling dynamic and incremental knowledge graph updates, and
leveraging domain-adapted or fine-tuned biomedical LLMs.
These directions are expected to further strengthen the
robustness, scalability, and applicability of the framework in real-
world biomedical and clinical environments.
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DALK: Dynamic Co-Augmentation of LLMs and KG to answer
Alzheimer’s Disease Questions with Scientific Literature
Dawei Li1*, Shu Yang2∗, Zhen Tan1, Jae Young Baik2, Sunkwon Yun3, Joseph Lee2, Aaron Chacko2,
Bojian Hou2, Duy Duong-Tran2,4, Ying Ding5, Huan Liu1†, Li Shen2†, Tianlong Chen3†
1School of Computing, and Augmented Intelligence, Arizona State University
2Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
3Department of Computer Science, The University of North Carolina at Chapel Hill
4Department of Mathematics, United States Naval Academy
5School of Information, The University of Texas at Austin, Austin
Abstract
Recent advancements in large language mod-
els (LLMs) have achieved promising perfor-
mances across various applications. Nonethe-
less, the ongoing challenge of integrating
long-tail knowledge continues to impede the
seamless adoption of LLMs in specialized do-
mains. In this work, we introduce DALK, a.k.a.
Dynamic Co-Augmentation of LLMs and KG,
to address this limitation and demonstrate its
ability on studying Alzheimer’s Disease (AD),
a specialized sub-field in biomedicine and a
global health priority. With a synergized frame-
work of LLM and KG mutually enhancing each
other, we first leverage LLM to construct an
evolving AD-specific knowledge graph (KG)
sourced from AD-related scientific literature,
and then we utilize a coarse-to-fine sampling
method with a novel self-aware knowledge re-
trieval approach to select appropriate knowl-
edge from the KG to augment LLM infer-
ence capabilities. The experimental results,
conducted on our constructed AD question
answering (ADQA) benchmark, underscore
the efficacy of DALK. Additionally, we per-
form a series of detailed analyses that can of-
fer valuable insights and guidelines for the
emerging topic of mutually enhancing KG and
LLM. We will release the code and data at
https://github.com/David-Li0406/DALK.
1
Introduction
Alzheimer’s Disease (AD) is a neurodegenerative
disorder characterized by progressive declines in
cognitive and functional status over a span of
decades (Report, 2023). However, current AD ther-
apy developments are facing critical challenges
due to the lack of knowledge and understanding of
the underlying etiological mechanisms of the dis-
ease. Although scientific literature and dedicated
biomedical databases could supply rich sources of
* Equal Constributions
† Corresponding authors
AD knowledge, manual review of relevant informa-
tion is impossible due to the large volume.
As large language models (LLMs) (Brown et al.,
2020; Zhang et al., 2022; Anil et al., 2023; Touvron
et al., 2023) with chain-of-thought (CoT)-based
prompting (Wei et al., 2022; Wang et al., 2022;
Tong et al., 2023; Yao et al., 2023; Besta et al.,
2023) demonstrate strong language capabilities
across various tasks, there have been attempts to
leverage LLMs-based systems in general biomedi-
cal and AD-related applications (Mao et al., 2023;
Li et al., 2023c; Yan et al., 2024; Feng et al., 2023).
However, while the LLMs have shown promising
performances in many general tasks, recent studies
revealed LLMs’ limitations in long-tail (Kandpal
et al., 2023) and domain-specific (Li et al., 2023b,
2024) knowledge, thereby significantly impeding
their adaptations in vertical fields such as AD. To
deal with this issue, the most common strategies are
retrieval augmented generation (RAG) and domain-
specific LLMs training.
Nevertheless, directly applying these strategies
in the context like AD would still suffer from sev-
eral issues.
First, Data Quality: As in many
biomedical fields, scientific literature composes
the largest publicly available corpus source in AD.
Yet, the dense and information-overloaded nature
of scientific literature, when combined with auto-
matic retrieval methods, can lead to the retrieval
of irrelevant and noisy information. Previous re-
search has shown that noisy and irrelevant corpora
can significantly undermine the performance of
LLMs (Yu et al., 2023; Chen et al., 2024; Wu et al.,
2024). Second, Efficiency & Scale Issues: Being
an critical field of research, the knowledge of AD
is rapidly evolving with scientific advancements at
a remarkable pace and scale. However, retraining
a domain-specific LLM or updating certain knowl-
edge in it demands substantial computational re-
sources (Hu et al., 2021; Ovadia et al., 2023; Zhang
et al., 2024). This efficiency issue would also limit
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the sizes of domain-specific LLMs, consequently
affecting their performances.
To tackle these limitations, here we propose
a Dynamic Co-Augmentation of LLMs and KG
(DALK) framework that facilitates mutual benefits
between LLMs and knowledge graphs (KG) for
the AD domain. Initially, our framework addresses
the data quality challenge by extracting more struc-
tural and accurate knowledge from unstructured
and dense scientific literature and constructing a
domain-specific knowledge graph tailored to AD.
We employ two widely utilized knowledge graph
construction methods, namely pair-wise construc-
tion (Carta et al., 2023; Wadhwa et al., 2023) and
generative construction (Han et al., 2023; Bi et al.,
2024), to comprehensively assess their impact on
knowledge graph quality. Then, we adopt a coarse-
to-fine sampling method with a novel self-aware
knowledge retrieval approach to select appropri-
ate knowledge from the knowledge graph and thus
further address the data quality problem. Notably,
the tuning-free nature of our framework signifi-
cantly enhances efficiency and facilitates its ap-
plication in large-scale and API-based language
models (OpenAI, 2022). In the evaluation sec-
tion, we derive an Alzheimer’s Disease question
answering (ADQA) benchmark from existing gen-
eral medical QA datasets with millions of sam-
ples filtered by a curated keyword list and self-
sampling of LLMs. Our extensive experiment on
ADQA demonstrates the effectiveness of our frame-
work in domain-specific applications compared
with general biomedical LLMs and retrieval aug-
mented models. Further evaluation and analysis
provide valuable insights into constructing high-
quality knowledge graphs and sampling accurate
knowledge from them.
In summary, our contribution in this work can
be summarized as follows:
• We identify the constraints of the current meth-
ods for LLMs in domain-specific areas like
AD and introduce DALK, a co-augmentation
framework of the LLM and KG to address
these issues.
• We build AD-specific KG and QA benchmark.
Through extensive comparisons with other
methods, we showcase the effectiveness of
DALK.
• We delve into a comprehensive analysis of
our proposed method and provide valuable
insights and guidance on how to construct a
high-quality KG and sample accurate knowl-
edge from it.
2
Related Work
The
interplay
between
LLMs
and
KGs
KGs (Miller, 1995; Speer et al., 2017; Vrandeˇci´c
and Krötzsch, 2014) serve as structured represen-
tations of factual knowledge, typically expressed
as (head, relation, tail) triples. Their structured,
factual, and interpretable nature renders them ex-
cellent complements to parametric language mod-
els (Pan et al., 2024). Recently, with the rise of
large language models (LLMs), numerous studies
have delved into exploring the synergy between
LLMs and KGs for various purposes (Pan et al.,
2024; Tan et al., 2024). There are a lot of efforts in
conducting knowledge graph construction (Carta
et al., 2023; Wadhwa et al., 2023; Han et al., 2023;
Bi et al., 2024; Datta et al., 2024), completion (Wei
et al., 2023; Zhang et al., 2023b; Li et al., 2024)
with the aid of LLMs. Conversely, other works aim
to enhance LLMs by integrating knowledge sam-
pled from KGs during both training (Tang et al.,
2023; Luo et al., 2024; Dernbach et al., 2024;
Rangel et al., 2024) and inference (Kim et al., 2023;
Wen et al., 2023; Jiang et al., 2023; Sun et al.,
2023a) times. Our work distinguishes itself by
proposing a co-augmentation framework for LLMs
and KGs, facilitating their mutual enhancement,
and applying it to the domain of AD.
LLMs and KGs for AD research
LLMs and
KGs have both been applied to Alzheimer’s Dis-
ease research in previous studies. Pre-trained lan-
guage models are utilized to work on AD de-
tection and many other related tasks based on
speech recordings and transcripts (Balagopalan
et al., 2020; Agbavor and Liang, 2022), electronic
health records (EHRs) (Mao et al., 2023; Li et al.,
2023c; Yan et al., 2024), and tabular data (Feng
et al., 2023).
KGs have been widely used in
biomedical research, yet only a few are specifically
for AD research (Romano et al., 2023; Pu et al.,
2023; Hsieh et al., 2023; Nian et al., 2022; Daluwa-
tumulle et al., 2023). These KGs were generally
constructed from a variety of information derived
from heterogeneous biomedical databases (e.g. for
genes, drugs, pathways, etc.) or scientific literature
related to AD. Despite the aforementioned efforts
for LLMs and KGs in AD research, no prior study
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vice versa, let alone the potential for mutual en-
hancement between the two as we propose here.
3
Our Methodology
This section elaborates on our dynamic co-
augmentation framework of LLMs and KG. Sec-
tion 3.1 presents the details of augmenting an AD-
specific evolving KG with LLMs and literature
corpus in a time-slicing fashion (i.e. year by year).
Following it, Section 3.2 describes the process of
sampling appropriate knowledge from the evolving
KG to enhance LLMs’ reasoning. Figure 1 illus-
trates an overall pipeline of our method DALK.
KGpair
KGgen
#Corpus
9,764
9,764
#Nodes
13,509
20,545
#Relations
3,952
3,651
#Triples
171,431
53,585
Table 1: Detailed statistics about our augmented knowl-
edge graph.
3.1
LLMs for KG
Corpus Collection
To create an AD-specific
knowledge graph, we follow (Pu et al., 2023) and
use the AD corpus collected by a domain expert
Professor Colin Masters at the University of Mel-
bourne who discovered amyloid proteins being the
potential cause of AD (Masters et al., 1985). The
corpus is based on his extensive bibliography of
representative AD-related papers and consists of
more than 16K PMID (PubMed ID)-indexed arti-
cles from 1977 to 2021. For our study, we focus on
the papers since 2011 which reflect the most recent
knowledge in the field and get 9,764 articles.
Entity Recognition
In order to identify knowl-
edge at the proper granularity level for AD, we
extract relevant entities from the corpus by utiliz-
ing the PubTator Central (PTC) (Wei et al., 2013)
developed and continuously maintained by NCBI.
PTC is a widely-used tool to provide state-of-the-
art annotations of biomedical concepts for PubMed
abstracts and full-text articles, and it supports six
bioconcept types including genes, diseases, chem-
icals, mutations, species and cell lines. We apply
PTC to the abstracts of all our AD papers and ob-
tain the relevant named entities which will serve as
nodes in the knowledge graph.
Relation Extraction
To build an accurate and
high-quality knowledge graph on AD, we aim to as-
sign a specific relation type between the two related
entities. Through a comprehensive survey of rela-
tion extraction methods for knowledge graph con-
struction, we categorize current approaches with
LLMs into two main groups: (a). Pair-wised Rela-
tion Extraction (Carta et al., 2023; Wadhwa et al.,
2023) aims to prompt the LLMs to describe the
relationship between any two entities in a segment
of text. (b). Generative Relation Extraction (Han
et al., 2023; Bi et al., 2024; Datta et al., 2024),
where LLMs directly output all related entity pairs
and their corresponding relationships. As shown
in Figure 2, we incorporate both of these rela-
tion extraction methods into our knowledge graph
augmentation process to provide a comprehensive
comparison between them. We denote the result-
ing knowledge graphs from these approaches as
KGpair and KGgen respectively.
Table 1 presents the detailed statistics about our
augmented knowledge graph, including the num-
ber of corpora we used, and the number of nodes,
relations and triples in KGpair and KGgen.
3.2
KG for LLMs
In this section, we begin by outlining our process
for sampling coarse-grained augmented knowledge
from our evolving knowledge graph (Section 3.2.1).
Subsequently, we delve into detail regarding our
self-aware knowledge retrieval method, which aims
to filter out noise and retrieve the most pertinent
knowledge to provide to the LLM (Section 3.2.2).
3.2.1
Coarse-grained Knowledge Sample
Given a question query Q, we first construct a
prompt and ask LLMs to extract all the domain-
specific entities E = {e1, e2, ...} from it.
Af-
terward, we adhere to the methodology proposed
by Wen et al. (2023) and execute a similarity-based
entity linking process to connect all entities within
E to the entity structure in our knowledge graph
G. Specifically, we employ a semantic similarity
model (Reimers and Gurevych, 2019) to encode all
entities in G and E into dense embeddings, denoted
as HG and HE, respectively. Subsequently, utiliz-
ing cosine similarity, we establish links between
each entity in E and its nearest neighbor entity in
G. This procedure yields an initial entity set EG
for the subsequent knowledge sampling step.
To build an evidence sub-graph to boost
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… Successful application of
AD preventative approaches
may hinge on an accurate and
comprehensive view of
comorbidities, including
cardiovascular disease,
diabetes, and head trauma.
Literature Corpus
LLMs for KG
KG for LLMs
Head
Relation
Tail
Diabetes
AD
Head trauma
…
…
…
risk factor
AD
risk factor
Extracted Triplets
Evolving KG
Evolving KG
AD
Cognition
level
Down’s
syndrome
Head
trauma
Diabetes
Selected Knowledge
LLMs
LLMs
Extract
Process
Augment
Q: Following are
predispositions to
Alzheimer's disease
except ___.
A). Down’s syndrome
B). Head trauma
C). Smoking
D). Low education group
Answer
Rerank
Sample
AD
Cognition
level
Down’s
syndrome
Head
trauma
Diabetes
Subgraph Sampling
Genetic
factors
Lifestyle
Input
Q: Following are
predispositions to
Alzheimer's disease
except ___.
A). Down’s syndrome
B). Head trauma
C). Smoking
D). Low education group
Question
Input
Figure 1: The overview pipeline of DALK. We first extract structural knowledge from unstructured corpora and
construct a domain-specific knowledge graph tailored to AD (Section 3.1). Then, we utilize a coarse-to-fine sampling
method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the knowledge
graph (Section 3.2).
… Successful application of AD preventative approaches… ,
including cardiovascular disease, diabetes, and head trauma.
… Successful application of AD preventative approaches… ,
including cardiovascular disease, diabetes, and head trauma.
Step1: Entity Recongnition
Step2: Relation Extraction
Pair-wised Relation Extraction
Generative Relation Extraction
…
Reading the following abstract: [Abstract]
Question:
What is the relationship between AD
and cardiovascular disease?
Question:
What is the relationship between AD
and diabetes?
Reading the following abstract: [Abstract]
Here are all the biomedicine-related
entities: [Entities]
Question:
Please extract all the valid relationship
between the provided entities.
Figure 2: The detailed process of AD-specific KG con-
struction.
study (Wen et al., 2023) and consider the following
two kinds of explorations in our AD-KG:
Path-based Exploration
entails the extraction of
a sub-graph from G to encompass all entities within
EG. The process unfolds as follows: (a) Begin
by selecting one node from e0
Q as the initial node,
denoted as e1, and place the remaining nodes into a
candidate node set, Ecand. Explore at most k hops
from e1 to identify the subsequent node, e2, where
e1 ∈Ecand. If e2 is successfully reached within
k hops, update the start node to e2 and remove e2
from Ecand. In the event e2 cannot be found within
k hops, concatenate the segment paths acquired
thus far and store them in Gpath
Q
. Subsequently,
choose another node e′
1 from Vcand as the new start
node, and eliminate both the original start node e1
and the current node e2 from Ecand. (b) Verify if
Ecand is empty. If not, repeat step (a) to identify
the next segment of the path. If Ecand is empty,
combine all segments to construct a set of sub-
graphs and place them into Gpath
Q
.
Neighbor-based Exploration
endeavors to aug-
ment the evidence relevant to the query within GQ.
This process consists of two steps: (a) Initially,
expand each node e within EG by 1-hop to incor-
porate their neighbors e′, thus appending triples
(e, r, e′) to Gnei
Q . (b) Then assess whether each e′
exhibits semantic relevance to the query. If affir-
mative, further expand the 1-hop neighbors of e′,
consequently adding triples (enei, r′, e′) to Gnei
Q .
After obtaining the two sub-graphs Gpath
Q
and
Gnei
Q , we perform post-processing to further prune
redundant information in sub-graphs and prompt
LLMs to describe the structure of each sub-graph.
3.2.2
Self-aware Knowledge Retrieval
In our initial experiment, we noticed the coarse-
grained knowledge sampled with the above-
mentioned approaches still contained redundant
and irrelevant information. This issue of noise is a
common challenge encountered in automatically-
constructed knowledge graphs (Fang et al., 2021;
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Moreover, many recent works (Yu et al., 2023; Li
et al., 2023d; Chen et al., 2024; Wu et al., 2024)
have demonstrated LLMs can indeed be influenced
by such noisy information. To address this chal-
lenge, we borrow insights from the recent self-
powered LLMs (Wang et al., 2022; Pan et al., 2023;
Li et al., 2023a; Yuan et al., 2024; Tong et al.,
2024) and propose a self-aware knowledge retrieval
method to leverage LLMs’ ranking capability (Sun
et al., 2023b; Ma et al., 2023) to filter out noisy
information.
In particular, we directly prompt the LLM to
rerank the sampled knowledge and only retrieve
top k triples to provide for itself in the final-round
inference. Given the question Q and either the path-
based or neighbor-based sub-graph GQ, we create
prompt pself by filling the pre-defined template:
pself = Templateself(Q, GQ, k).
(1)
Then, we use pself as the input to prompt the LLM
to obtain the self-retrieved knowledge:
Gself
Q
= LLM(pself),
(2)
Finally, we provide the question Q and fine-grained
knowledge Gself
Q
to the LLM for reasoning and get
the predicted answer a in two steps:
pinference = Templateinference(Q, Gself
Q
),
(3)
a = LLM(pinference).
(4)
We provide detailed examples in Appendix A and B
to demonstrate the input and output in our DALK.
4
Main Experiment
4.1
ADQA Benchmark
For performance evaluation, we consider four
widely-used medical QA datasets spanning diverse
biomedical topics (Jin et al., 2021; Pal et al., 2022;
Hendrycks et al., 2021; Peñas et al., 2013) and de-
rive an AD-specific QA dataset from them. The
four medical QA datasets are all multiple-choice
based and include: 1) MedQA (Jin et al., 2021)
consisting of US Medical Licensing Examina-
tion (USMLE)-style questions, 2) MedMCQA (Pal
et al., 2022) containing medical school entrance
exam questions from India, 3) MMLU (Hendrycks
et al., 2021) consisting of diverse biomedical
and clinical questions from various sources, 4)
QA4MRE (Peñas et al., 2013) containing a sub-
set of questions for AD derived from PubMed
and Medline. In order to extract from the med-
ical QA datasets a subset of samples related to
AD for our evaluation, we referred to NIH’s Com-
mon Alzheimer’s and Related Dementias Research
Ontology (CADRO) 1. Jointly developed by the
National Institute on Aging and the Alzheimer’s
Association, CADRO is a three-tiered classifica-
tion system with eight main categories and a dozen
sub-categories for AD and related dementia, and it
contains common terminologies or keywords used
in the field. We derived from the CADRO a list of
AD-related keywords most relevant to the medical
QA datasets: <Aging, Alzheimer, Amyloid beta,
APOE, Dementia, Lipoprotein, Microglia>. Then,
we searched against each medical QA dataset for
matches with these keywords to find putative QA
samples, then further asked GPT-3.5-turbo to judge
for each putative sample whether the question is
indeed related to AD or not. Finally, we filtered
out a subset of such samples that are considered
highly relevant to AD to conduct our evaluation
(number of samples in each dataset is shown in
Table 2). More details about ADQA can be found
in Appendix C.
4.2
Experiment Settings
We apply our framework with OpenAI GPT-3.5-
turbo models (OpenAI, 2022). We also include the
following baseline methods for comparison:
Biomedical LLMs
Both ChatDoctor (Yunxiang
et al., 2023) and Med-Alpaca (Shu et al., 2023)
are fine-tuned versions of LLaMA (Touvron et al.)
on biomedical corpora.
Compared with them,
Meditron (Chen et al., 2023) is built on LLaMA-
2 (Touvron et al., 2023) and extends its pretrain-
ing on a comprehensively curated medical cor-
pus. BiomedGPT (Zhang et al., 2023a) is also
based on LLaMA-2 and pioneer as the first open-
source and generalist visual language AI for di-
verse biomedical tasks. Biomistral (Labrak et al.,
2024) is an open-source LLM crafted specifically
for the biomedical domain, optimized for effi-
ciency through quantization and model merging
techniques.
Retrieval-Augmented LLMs
Furthermore, we
also compare our method with several represen-
tative retrieval-augmented LLMs in the biomed-
ical domain. Almanac (Zakka et al., 2024) is a
novel approach utilizing OpenAI’s GPT model in-
1https://iadrp.nia.nih.gov/about/cadro | {
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MedQA
MedMCQA
MMLU
QA4MRE
AVG
Biomedical LLMs
ChatDoctor (Yunxiang et al., 2023)
25.7
36.4
46.9
51.4
40.1
Med-Alpaca (Shu et al., 2023)
41.4
42.8
44.9
57.1
46.5
BiomedGPT (Zhang et al., 2023a)
38.8
41.9
48.9
42.6
43.1
Meditron (Chen et al., 2023)
27.6
31.4
36.7
25.7
30.4
Biomistral (Labrak et al., 2024)
44.7
49.5
53.1
68.6
54.0
Retrieval-augmented LLMs
GPT-3.5-turbo w/ Ada (OpenAI, 2024)
57.2
65.7
83.7
62.9
67.4
Almanac (Zakka et al., 2024)
48.0
69.5
71.4
60.0
62.2
Clinfo.ai (Lozano et al., 2023)
54.3
77.0
81.3
67.7
70.1
Clinfo.ai w/o PubMed API
49.3
68.6
79.6
74.3
67.9
GPT-3.5-turbo
50.0
71.9
83.6
62.9
67.1
DALK
57.9
75.2
85.4
71.4
72.6
Table 2: Experiment results on our constructed ADQA benchmark. The sample size is indicated after each dataset
name. The best results of each metric are in bold and the second-best results are underlined. The “AVG” column
represents the average accuracy score on the four sub-dataset.
tegrated with a Qdrant vector database to hold ex-
ternal sources of knowledge retrieved from local
corpus, web search, and calculators, designed to
answer open-domain clinical questions. Like Al-
manac, Lozano et al. (2023) introduced Clinfo.ai,
which is an open-source, end-to-end retrieval-
augmented LLM (GPT) to answer medical queries
using scientific literature summarizations derived
from PubMed search engine. We adopt both Al-
manac and Clinfo.ai with the same prompt as ours
to answer multiple-choice questions to suit the
ADQA benchmark. Lastly, we implement a sim-
ple retrieval-augmented GPT baseline with CoT
prompting similar to our proposed DALK. All the
GPT models used are set to GPT-3.5-turbo as de-
tailed in the next paragraph, to be consistent.
Implementation Details
We use the knowledge
graph constructed with the generative approach
(KGgen) in our main experiment and conduct an
ablation study on the knowledge graph with RE
method (KGpair) in Section 4.5. We use GPT-3.5-
turbo with the version “gpt-3.5-turbo-0301” and
set the sampling temperature to 0.7. We utilize 7B
versions of all the biomedical LLMs baselines. For
RAG methods, we split each document with a max
length of 128 and retrieve the top 3 most relevant
documents as the support evidence for LLMs to
do inference. We set the parameter k in our self-
aware knowledge retrieval to 5 and conduct further
analysis on it in Section 5.2.
4.3
Main Result
Table 2 shows the experimental results on our
ADQA benchmark. We note that upon applying our
dynamic co-augmentation framework, DALK’s per-
formance surpasses that of other biomedical LLMs
and RAG methods overall. It consistently achieves
either the best or the second-best accuracy score
across all sub-datasets and attains the highest AVG
score. Furthermore, the substantial improvement
over vanilla GPT-3.5-turbo underscores the efficacy
of our approach in domain-specific ADQA.
Furthermore, we observe that the performance
of biomedical-specific LLMs generally lags be-
hind that of GPT-3.5-turbo. We attribute this dis-
crepancy to the smaller size of these biomedical
LLMs. While they may perform adequately in gen-
eral medical contexts, they fall short in the AD
scenario, which demands more domain-specific
knowledge. In the case of GPT-3.5-turbo combined
with various RAG methods, it is evident that most
RAG methods enhance the models’ performance.
Among them, GPT-3.5-turbo with Clinfo.ai yields
the most significant improvement, boosting the ac-
curacy score from 67.1 to 70.1 compared to vanilla
GPT-3.5-turbo. However, it is important to note
that the original Clinfo.ai necessitates access to
the PubMed API, constituting an external resource.
When we disable this access and solely utilize the
same corpora as in DALK within the Clinfo.ai re-
trieval system, the improvement it brings becomes
marginal and incomparable to our method. Due to
the space limitation, we put more RAG results with
different hyper-parameters in Appendix D.
4.4
Ablation Study on Self-aware Knowledge
Retrieval
In this section, we evaluate the efficacy of our
proposed self-aware knowledge retrieval method
through an ablation study.
As depicted in Ta-
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MedQA
MedMCQA
MMLU
QA4MRE
AVG
AVG Length
107.4
23.8
342.9
17.6
122.9
GPT-3.5-turbo
50.0
71.9
83.6
62.9
67.1
DALK
57.9
75.2
85.4
71.4
72.6
DALK
w/o self-aware
knowledge retrieval
56.5
71.0
77.6
77.1
70.6
Table 3: Ablation study results with and without our
proposed self-aware knowledge retrieval.
knowledge retrieval module still enhances the
model’s performance, the overall improvement is
less pronounced. Furthermore, we observe that the
efficacy of self-aware knowledge retrieval corre-
lates with the length of queries within a given con-
text. For instance, a notable enhancement in per-
formance is evident within the MMLU sub-dataset
upon the implementation of self-aware knowledge
retrieval. We attribute this to the fact that questions
in the MMLU dataset typically contain longer con-
texts compared to other medical QA datasets inte-
grated into ADQA. Consequently, irrelevant knowl-
edge sourced from the context may exacerbate the
issue of information noise thus underscoring the ne-
cessity for self-aware retrieval. Conversely, within
QA4MRE, characterized by shorter query lengths,
the application of self-aware knowledge retrieval
can even lead to a decline in performance.
4.5
Ablation Study on KG Construction
AVG
#Triples
GPT-3.5-turbo
67.1
-
DALK
w/ Generative KG
72.6
53,585
DALK
w/ RE KG
66.3
171,431
Table 4: Ablation study results with generative construc-
tion and RE construction.
Table 4 illustrates the results of the ablation study
conducted using generatively constructed KG and
RE-constructed KG. Surprisingly, despite the RE
method yielding a KG with a larger scale and more
triples, knowledge sampled from it has unexpect-
edly resulted in a non-trivial drop in performance
within ADQA. After a manual examination of the
two constructed knowledge graphs, we find LLMs
with the RE construction method have a strong in-
clination to wrongly assign a relationship to two
unrelated entities, which has been exposed by the
previous studies (Wan et al., 2023). In contrast, the
generative construction approach exclusively out-
puts triples that LLMs confidently endorse, yield-
ing a smaller yet more precise knowledge graph.
This trade-off between coverage and accuracy un-
derscores the critical importance of denoising in
the construction of KGs by LLMs.
5
Further Analysis
5.1
Co-augmentation Analysis
Figure 3: The size of the knowledge graph (triplet num-
ber) and the KG-augmented GPT-3.5-turbo’s perfor-
mance (accuracy) over time.
To comprehensively understand how the perfor-
mance of LLMs evolves in response to increasing
KG sizes, we undertake a detailed co-augmentation
analysis. Illustrated in Figure 3, our experiments
aim to discern the changing performance trends of
LLMs as the knowledge triples accumulate annu-
ally. Our findings reveal that our framework effec-
tively fosters the co-evolution of LLMs and KG,
with the performance of KG-augmented LLMs ex-
hibiting a generally upward trajectory as the KG
expands. Notably, when we remove the self-aware
knowledge retrieval module, this upward trend be-
comes less significant. This further implies the
importance of sampling and selecting appropriate
knowledge for LLMs when the KG’s size increases.
5.2
Hyper-parameter Analysis
In this section, we do a hyper-parameter analy-
sis on the retrieval number k of our self-aware re-
trieval module. We select a group of value for k
([1,3,5,10,20,30]) and present the experiment re-
sults in Figure 4. We show the accuracy score on
MedQA, MedMCQA, QA4MRE and AVG with
different k. We find when k is small, an incre-
ment to it can lead to a performance enhancement.
After the best performance shows up, continually
increasing the value of k will cause a smooth de-
crease in the model accuracy score. This result
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Path-based Sub-graph
Answer
Baseline
-
C %
DALK
-w/o self-aware
knowledge retrieval
neurofibrillary tangles->FORM BY->microtubule-associated protein tau...
...
entorhinal cortex->is a part of->brain->ASSOCIATES->mouse with
Alzheimer’s disease->brain region->temporal lobe
C %
DALK
Reranked Triples1: entorhinal cortex ->is a part of ->brain
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
D !
Table 5: A case to show the effectiveness of DALK. The question is: “The area of the brain resistant to Neurofibrillary
tangles of Alzheimer’s disease is: A. Visual association areas B. Entorhinal coex C. Temporal lobe D.Lateral
geniculate body”
Figure 4: Different self-aware knowledge retrieval set-
tings in MedQA, MedMCQA, QA4MRE and the aver-
age score (AVG).
is more helpful while the knowledge ranked behind
is something not very useful, thus successfully val-
idating the capability of LLMs to do a fine-grained
knowledge reranking.
Moreover, we find the best k value is correlated
with the length of queries in each single dataset. For
example, the best performance in MedQA (average
query length is 107.4) shows up when k = 10 while
the best performance in MedMCQA and QA4MRE
shows up when k = 5 and 3 respectively. This is
consistent with our findings in Section 4.4 that a
longer query corresponds to a larger and noisier
sub-knowledge graph.
5.3
Sensitivity Analysis on ADQA Benchmark
In this section, we conduct a sensitivity analysis
for our constructed ADQA by conducting a leave-
one-out evaluation on AD-related keywords. We
do it by removing the samples with each keyword
in our keyword list and calculating the AVG score
of the remaining samples. As the result shown
in Table 6, we find not all of the keywords are
incorporated in our ADQA benchmark. Notably,
Benchmark
DALK
DALK
w/o self-aware
knowledge retrieval
ADQA
72.6
70.6
w/o “Alzheimer”
72.1
70.4
w/o “Dementia”
72.4
71.3
w/o “APOE”
73.2
71.2
w/o “Amyloid beta”
73.5
70.7
w/o “Aging”
72.9
71.4
w/o “Lipoprotein”
73.1
71.0
w/o “Microglia”
72.8
70.9
Table 6: Sensitivity analysis for ADQA benchmark with
a leave-one-out evaluation on AD-related keywords.
the keywords “CSF Biomarkers”, “Neurogenesis”,
“PET Amyloid”, “PET Tau”, “Tau Phosphorylation”
lack corresponding samples in ADQA. We believe
one critical work in the future for benchmarking
AD-related knowledge is to collect QA samples
to cover these missing keywords. Moreover, ana-
lyzing the performance variation upon removing
samples linked to each keyword offers insight into
determining the relevance of the keyword to AD.
5.4
Case Study
We put an example in Table 5 to showcase the effi-
cacy of DALK. We notice while the path-based sub-
graph contains the relevant knowledge to exclude
option C, it still involves other irrelevant informa-
tion and finally fails to prompt the LLMs to pro-
duce the correct answer. In contrast, our self-aware
knowledge retrieval method successfully chooses
the top 3 most relevant triples for the given problem
and results in the correct answer D.
6
Conclusion
In this research, we begin by analyzing the main
limitations of adopting the existing LLMs-based
methods in AD-specific areas. To address these
issues, we propose a novel approach in the merging
of large language models and knowledge graphs
in the context of Alzheimer’s Disease. Our team | {
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provides an innovative dynamic co-augmentation
framework for the refinement of large language
models and knowledge graphs. Initially, our ap-
proach extracts structural insights from the unstruc-
tured scientific literature, crafting a specialized
knowledge graph for AD. Subsequently, we employ
a coarse-to-fine sampling technique coupled with
a unique self-aware knowledge retrieval strategy
to pinpoint relevant information from the knowl-
edge graph. The extensive evaluation conducted
in our constructed ADQA benchmark showcases
the effectiveness of our method and provides fur-
ther hints into the synergy of LLMs and knowledge
graph in the context of AD. In the future, we will
do more exploration in adopting and benchmarking
LLMs in the AD areas.
7
Limitations
In the development of our AD-KG, our primary
focus lies in the exploration of two distinct meth-
ods for extracting relationships between associated
entities. For entity recognition, we employ a strong
PubTator annotator directly, without delving into
the utilization of LLMs in this context. However,
we have observed that LLMs also exhibit promis-
ing entity extraction capabilities in Section 3.2.1.
We defer the refinement of methods for extract-
ing entities for KG construction with LLMs to fu-
ture works. Furthermore, a significant contribution
of our work is the establishment of the ADQA
benchmark. Nonetheless, the datasets utilized in
constructing ADQA primarily consist of medical
school exam questions, potentially exhibiting a do-
main gap from the scientific literature informing
AD-KG. One potential remedy is leveraging Pub-
medQA (Jin et al., 2019); however, it is hindered
by limited data amount. In the future, we will keep
gathering AD-related QA samples and expanding
the size of our ADQA benchmark.
8
Ethics Statement
We have familiarized ourselves with and honour
the ethical code set out in the ACL Code of Ethics2.
The knowledge graphs constructed in the paper
are based on published scientific literature from
PubMed. The ADQA dataset used in the study is
also derived from publicly available medical QA
datasets that are properly cited. We strive to ensure
our study upholds ethical principles and not cause
any kind of safety or privacy concerns. Although
2https://www.aclweb.org/portal/content/acl-code-ethics
not observed in our multiple-choice QA analysis,
we recognize the possibility of factual errors and
hallucinations when using pre-trained LLMs for
medical QA tasks in general, and we do not recom-
mend these models be applied in a practical setting
at present.
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A
Details of LLMs for KG
Table 7 and 8 present examples of our two KG construction methods respectively. For both methods, we
adopt a select-or-generate prompt to instruct the LLM whether to choose a relation from hetionet (Him-
melstein et al., 2017), a well-built general medical KG, or generate a new one to describe the relationship
between two entities. In the RE construction method, we also conduct a type matching (Table 9) for each
entity from type name of PubTator to that of Hetionet and ask the LLM to choose from the relation set
that corresponds to the two entities’ types (Table 10).
Input
Read the following abstract, extract the relationships between each entity.You can
choose the relation from: (covaries, interacts, regulates, resembles, downregulates,
upregulates, associates, binds, treats, palliates), or generate a new predicate to
describe the relationship between the two entities. Output all the extract triples in
the format of "head | relation | tail". For example: "Alzheimer’s disease | associates
| memory deficits" Abstract: Thiamine pyrophosphate (TPP) and the activities of
thiamine-dependent enzymes are reduced in Alzheimer’s disease (AD) patients.
In this study, we analyzed the relationship between thiamine deficiency (TD) and
amyloid precursor protein (APP) processing in both cellular and animal models of
TD. In SH-SY5Y neuroblastoma cells overexpressing APP, TD promoted maturation
of beta-site APP cleaving enzyme 1 (BACE1) and increased beta-secretase activity
which resulted in elevated levels of beta-amyloid (Abeta) as well as beta-secretase
cleaved C-terminal fragment (beta-CTF). An inhibitor of beta-secretase efficiently
reduced TD-induced up-regulation of Abeta and beta-CTF. Importantly, thiamine
supplementation reversed the TD-induced alterations. Furthermore, TD treatment
caused a significant accumulation of reactive oxygen species (ROS); antioxidants
suppressed ROS production and maturation of BACE1, as well as TD-induced
Abeta accumulation. On the other hand, exogenous Abeta(1-40) enhanced TD-
induced production of ROS. A study on mice indicated that TD also caused Abeta
accumulation in the brain, which was reversed by thiamine supplementation. Taken
together, our study suggests that TD could enhance Abeta generation by promoting
beta-secretase activity, and the accumulation of Abeta subsequently exacerbated
TD-induced oxidative stress.Entity: Thiamine (thiamine), deficiency increases beta-
secretase, TPP (Thiamine pyrophosphate), Alzheimer’s disease (AD), patients,
amyloid precursor protein (beta-amyloid (Abeta)), neuroblastoma, BACE1 (beta-site
APP cleaving enzyme 1), Abeta, ROS (reactive oxygen species), Abeta accumulation,
mice. Output:
Output
Thiamine deficiency | downregulates | TPP, Thiamine deficiency | regulates |
Alzheimer’s disease, Alzheimer’s disease | involves | beta-secretase, Amyloid precur-
sor protein | produces | beta-amyloid, Amyloid precursor protein | in | neuroblastoma,
BACE1 | is | beta-secretase, BACE1 | regulates | beta-amyloid, Abeta | is | beta-
amyloid, Abeta | in | mice, Abeta | causes | reactive oxygen species, Abeta | regulates
| ROS
Table 7: An example to extract knowledge triples using generative construction method. | {
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Input
Read the following abstract, answer the following question. Abstract: Thiamine
pyrophosphate (TPP) and the activities of thiamine-dependent enzymes are reduced
in Alzheimer’s disease (AD) patients. In this study, we analyzed the relationship
between thiamine deficiency (TD) and amyloid precursor protein (APP) process-
ing in both cellular and animal models of TD. In SH-SY5Y neuroblastoma cells
overexpressing APP, TD promoted maturation of beta-site APP cleaving enzyme 1
(BACE1) and increased beta-secretase activity which resulted in elevated levels of
beta-amyloid (Abeta) as well as beta-secretase cleaved C-terminal fragment (beta-
CTF). An inhibitor of beta-secretase efficiently reduced TD-induced up-regulation
of Abeta and beta-CTF. Importantly, thiamine supplementation reversed the TD-
induced alterations. Furthermore, TD treatment caused a significant accumulation
of reactive oxygen species (ROS); antioxidants suppressed ROS production and
maturation of BACE1, as well as TD-induced Abeta accumulation. On the other
hand, exogenous Abeta(1-40) enhanced TD-induced production of ROS. A study
on mice indicated that TD also caused Abeta accumulation in the brain, which
was reversed by thiamine supplementation. Taken together, our study suggests that
TD could enhance Abeta generation by promoting beta-secretase activity, and the
accumulation of Abeta subsequently exacerbated TD-induced oxidative stress.Entity:
Thiamine (thiamine), deficiency increases beta-secretase, TPP (Thiamine pyrophos-
phate), Alzheimer’s disease (AD), patients, amyloid precursor protein (beta-amyloid
(Abeta)), neuroblastoma, BACE1 (beta-site APP cleaving enzyme 1), Abeta, ROS
(reactive oxygen species), Abeta accumulation, mice. Question: predict the relation-
ship between Disease entity "sclerosis" and Disease entity "multiple sclerosis", first
choose from the following options: A. resembles B. no-relation C. others, please
specify by generating a short predicate in 5 words. Answer: Let’s think step by step:
Output
1. Sclerosis is a disease of the central nervous system. 2. Multiple sclerosis is
the most common form of sclerosis. 3. So the relationship between sclerosis and
multiple sclerosis should be "sclerosis is a kind of multiple sclerosis". So the answer
is: A. resembles. So the answer is: A. resembles
Table 8: An example to extract the relationship between “sclerosis” and “multiple sclerosis” using RE construction
method.
PubTator Type
Hetionet Type
Gene
genes
Chemical
compounds
Disease
diseases
Table 9: Entity type match from PubTator to Hetionet.
Type-Type
Relations
genes-genes
["covaries", "interacts", "regulates"]
disease-disease
["resembles"]
compounds-compounds
["resembles"]
genes-diseases
["downregulates","associates","upregulates"]
genes-compounds
["binds", "upregulates", "downregulates"]
compounds-diseases
["treats", "palliates"]
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B
Details of KG for LLMs
In this section, we provide detailed input and output for adopting KG to augment LLMs, including
path-based and neighbor-based sub-graph sampling results (Table 11), self-aware knowledge retrieval
(Table 12), describing sub-graphs with LLMs (Table 13) and inference with sampled knowledge (Table 14).
The question we showcase here is “The area of the brain resistant to Neurofibrillary tangles of Alzheimer’s
disease is: A. Visual association areas B. Entorhinal coex C. Temporal lobe D.Lateral geniculate body”,
which same as the one we use in Section 5.4.
Path-based Sub-graph
neurofibrillary tangles->FORM BY->microtubule-associated protein tau->BINDS->
(18)F-THK-5117->ADMINISTERED TO->rats->has->Alzheimer’s disease ->
Alzheimer’s disease ->affects->human->has->AD->DISEASE OF->Brain
entorhinal cortex->is a part of->brain->ASSOCIATES->
mouse with Alzheimer’s disease->brain region->temporal lobe
Neighbor-based Sub-graph
neurofibrillary tangles->FORM BY->microtubule-associated protein tau
Alzheimer’s disease ->causes->neuronal death
Alzheimer’s disease ->associates->cognitive decline
Alzheimer’s disease ->affects->human
Alzheimer’s disease ->has subtype->neurodegenerative diseases
Table 11: An example of path-based and neighbor-based sub-graph for the question.
Input
There is a question and some knowledge graph. The knowledge graphs follow entity->relationship
->entity list format.
Graph:
neurofibrillary tangles->FORM BY->microtubule-associated protein tau->BINDS->
(18)F-THK-5117->ADMINISTERED TO->rats->has->Alzheimer’s disease ->Alzheimer’s disease ->
affects->human->has->AD->DISEASE OF->Brain
entorhinal cortex->is a part of->brain->ASSOCIATES->
mouse with Alzheimer’s disease->brain region->temporal lobe}
Question:
The area of the brain resistant to Neurofibrillary tangles of Alzheimer’s disease is:
A. Visual association areas
B. Entorhinal coex
C. Temporal lobe
D.Lateral geniculate body
Please rerank the knowledge graph and output at most 5 important and relevant triples for
solving the given question. Output the reranked knowledge in the following format:
Reranked Triple1: xxx ——>xxx
Reranked Triple2: xxx ——>xxx
Reranked Triple3: xxx ——>xxx
Reranked Triple4: xxx ——>xxx
Reranked Triple5: xxx ——>xxx
Answer:
Output
Reranked Triples1: entorhinal cortex ->is a part of ->brain
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
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Input
There are some knowledge graph paths. They follow entity->relationship->entity format.
Reranked Triples1: entorhinal cortex ->is a part of ->brain
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
Use the knowledge graph information. Try to convert them to natural language, respectively.
Use single quotation marks for entity name and relation name.
And name them as Path-based Evidence 1, Path-based Evidence 2,...
Output:
Output
Path-based Evidence 1: ’Entorhinal cortex’ is a part of ’brain’.
Path-based Evidence 2: ’Entorhinal cortex’ associates ’mouse’ with ’Alzheimer’s disease’.
Path-based Evidence 3: ’Temporal lobe’ is affected by ’Alzheimer’s disease’.
Table 13: An example to describe the self-retrieved path-based sub-graph with the LLM.
Input
Question: The area of the brain resistant to Neurofibrillary tangles of Alzheimer’s disease is:
A.Visual association areas
B.Entorhinal coex
C.Temporal lobe
D.Lateral geniculate body
You have some medical knowledge information in the following:
###Path-based Evidence 1: ’Entorhinal cortex’ is a part of ’brain’.
Path-based Evidence 2: ’Entorhinal cortex’ associates ’mouse’ with ’Alzheimer’s disease’.
Path-based Evidence 3: ’Temporal lobe’ is affected by ’Alzheimer’s disease’.
###Neighbor-based Evidence 1: ’neurofibrillary tangles’ are ’FORMED BY’ ’
microtubule-associated protein tau’.
Neighbor-based Evidence 2: ’Alzheimer’s disease’ ’CAUSES’ ’neuronal death’.
Neighbor-based Evidence 3: ’Alzheimer’s disease’ ’ASSOCIATES’ with ’cognitive decline’.
Neighbor-based Evidence 4: ’Alzheimer’s disease’ ’AFFECTS’ ’human’.
Neighbor-based Evidence 5: ’Alzheimer’s disease’ ’HAS SUBTYPE’ ’neurodegenerative diseases’."
Answer: Let’s think step by step:
Output
In Alzheimer’s disease, neurofibrillary tangles are composed of
abnormal aggregations of tau protein within neurons, leading to
neuronal dysfunction and eventual cell death. The lateral geniculate
body is a relay center in the thalamus that processes visual
information from the retina to the visual cortex. Since the lateral
geniculate body is not directly involved in memory and cognitive
function, it is less likely to be affected by neurofibrillary tangles in
Alzheimer’s disease compared to other brain regions such as the
entorhinal cortex and temporal lobe which play crucial roles in memory
and cognitive processing. Therefore, the area of the brain resistant to
neurofibrillary tangles of Alzheimer’s disease is the lateral geniculate
body (option D)
Table 14: An example of inference with selected knowledge
C
ADQA Benchmark
Table 15 shows the detailed prompt of our self-sample method to construct ADQA benchmark. Table 16
gives the statistics of our ADQA benchmark.
Input
Judge whether the question below is related to Alzheimer’s Disease. Please answer yes or no.
Question: Treatable causes of dementia are ___.
a).AD b).Hypothyroidism c).Multi-infarct dementia d).SDH e).Hydrocephalus
Is the question related to Alzheimer’s Disease? Answer:
Output
Yes
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Dataset
MedQA
MedMCQA
MMLU
QA4MRE
Total
Number
152
210
49
35
446
Table 16: Statistics of our ADQA benchmark.
D
Further Experiment for RAG
MedQA
MedMCQA
NMMLU
QA4MRE
AVG
Almanac w/ 256 chunk size
50.0
69.0
67.3
62.9
62.3
Almanac w/ top 10 docuemnt
48.7
68.6
65.3
62.9
61.4
Almanac w/ CoT
50.0
65.7
77.6
65.7
64.7
Clinfo.ai w/ 256 chunk size
48.6
66.7
81.6
65.7
65.7
Clinfo.ai w/ top 5 docuemnt
43.4
68.1
77.6
68.6
64.4
Clinfo.ai w/ CoT
48.7
68.6
79.6
68.6
65.0
Table 17: Further experiment in RAG methods with different hyper-parameter settings. | {
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Databases and ontologies
BioThings Explorer: a query engine for a federated
knowledge graph of biomedical APIs
Jackson Callaghan
1,†, Colleen H. Xu
1,†, Jiwen Xin1,†, Marco Alvarado Cano1,
Anders Riutta
2, Eric Zhou1, Rohan Juneja1, Yao Yao1, Madhumita Narayan1,
Kristina Hanspers2, Ayushi Agrawal
2, Alexander R. Pico2, Chunlei Wu
1,*, Andrew I. Su
1,*
1Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, United States
2Data Science and Biotechnology, Gladstone Institutes, University of California, San Francisco, CA 94158, United States
*Corresponding authors. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, United States.
E-mails: cwu@scripps.edu (C.W.) and asu@scripps.edu (A.I.S.)
†These authors contributed equally to this work.
Associate Editor: Peter N. Robinson
Abstract
Summary: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can
easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowl-
edge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects,
and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sour-
ces. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated infor-
mation in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for
each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized
knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time.
Availability and implementation: More information can be found at https://explorer.biothings.io and code is available at https://github.com/bio
things/biothings_explorer.
1 Introduction
While downloadable files are the most common way to share
biomedical data, application programming interfaces (APIs)
are another popular and powerful mechanism for data dis-
semination. Accessing data through APIs has many comple-
mentary advantages relative to downloading local copies of
data. APIs typically allow users to query for specific subsets
of the data that are of interest. API queries are often highly
indexed, leading to efficient data retrieval. Finally, API access
allows for easy incorporation of the most up-to-date data into
other computational applications or workflows.
While APIs offer many advantages in terms of data acces-
sibility, these advantages do not immediately translate into
efficient data integration. APIs generally follow some com-
mon architectures and protocols [e.g. representational state
transfer (REST), output in JavaScript Object Notation
(JSON)], but alignment at this technical level does not guar-
antee either syntactic or semantic interoperability. For ex-
ample, APIs can use different identifiers for the same gene,
different data structures to represent gene attributes, and
different terms to describe the relationships between bio-
medical entities.
There have been some efforts to define and enforce seman-
tic and syntactic standards to achieve data interoperability.
Examples of this approach include the Beacon API from the
GA4GH consortium (Rambla et al. 2022) and the DAS speci-
fication for sharing annotations of genomic features (Dowell
et al. 2001). These efforts rely on the active participation of
API developers in adopting a community standard for their
API endpoints.
Here, we explore the use of semantically precise API anno-
tations as a complementary approach to achieving API inter-
operability. We divided this work into two components. First,
we created an extension of the widely used OpenAPI annota-
tion standard (http://spec.openapis.org/oas/v3.1.0) to seman-
tically describe the APIs’ inputs and outputs, and a registry to
organize these API annotations. Second, we created an appli-
cation called BioThings Explorer to consume the API meta-
data and to execute multi-hop graph queries that span
multiple APIs. Together, this architecture allows users to
query a large, federated knowledge graph based on an inter-
connected network of biomedical APIs. This federated design
offers a unique approach for creating knowledge graphs that
is complementary to the more common strategy of centraliza-
tion and local data integration.
Received: 18 April 2023; Revised: 18 August 2023; Editorial Decision: 11 September 2023; Accepted: 12 September 2023
V
C The Author(s) 2023. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Bioinformatics, 2023, 39(9), btad570
https://doi.org/10.1093/bioinformatics/btad570
Advance Access Publication Date: 14 September 2023
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2 A registry of semantically annotated APIs
The first step in creating a network of interoperable APIs is to
annotate each API in a semantically precise way. We built this
API annotation system on the OpenAPI specification, the de
facto standard for documenting API metadata in a human-
and machine-readable format. OpenAPI describes basic API
metadata (e.g. title, description, version, contact info), as well
as key information on the operation of the API endpoints (e.g.
server URL, endpoint input parameters, endpoint response
schemas).
However, this basic OpenAPI specification does not include
key domain-specific information that is necessary to facilitate
downstream API interoperability. Therefore, we defined an
OpenAPI extension to capture semantically precise annota-
tions of each API endpoint. These annotations include the se-
mantic types and identifier namespaces of biomedical entities
that are both used in querying (inputs) and found in the re-
sponse (outputs), the JSON path to the output identifier val-
ues in the JSON response, and the predicate describing the
relationship
between
the
input
and
output
entities
(Supplementary Fig. S1).
We also chose a strategy to map arbitrary JSON data struc-
tures to an established biological data model. In this effort,
we mapped API output to the Biolink Model (Unni et al.
2022), a community-developed data model that was adopted
and extended by the NCATS Translator consortium (Fecho
et al. 2022). The Biolink model defines the universe of seman-
tic types that can be used in BioThings Explorer, the allowed
identifier systems for each semantic type and the allowed
predicates. We provided a guide to writing these API annota-
tions in our GitHub repository (https://github.com/biothings/
biothings_explorer/blob/main/docs/README-writing-x-bte.
md).
To annotate and catalog APIs with our OpenAPI extension,
we leveraged the SmartAPI registry (https://smart-api.info/)
(Zaveri et al. 2017). We created SmartAPI registrations for 34
APIs with semantic annotations. The selection of these APIs
that are queried by BioThings Explorer is set in a local,
instance-specific configuration file. This collection of API
annotations can be thought of as a “meta-knowledge graph”
(meta-KG), where the nodes represent types of biomedical en-
tities (genes, diseases, drugs) and the edges represent APIs that
describe relationships between two types of biomedical enti-
ties. The SmartAPI meta-KG currently contains 35 nodes and
1817
edges.
(The
complete
meta-KG
is
shown
in
Supplementary Table S1 and a partial rendering is shown in
Fig. 1.)
3 API interoperability using BioThings Explorer
The second step in creating our federated biomedical knowl-
edge graph was to create BioThings Explorer, an engine to au-
tonomously
query
the
SmartAPI
meta-KG,
query
the
annotated APIs to retrieve associations between biomedical
entities, and integrate those APIs’ responses. The input to
BioThings Explorer is a query graph and the syntax for
encoding the query graph was defined by the NCATS
Translator consortium (Fecho et al. 2022). The topology of
the query graph and the constraints on its nodes and edges de-
fine the query (Fig. 2).
BioThings Explorer executes the query in three distinct
phases: query-path planning, query-path execution, and inte-
gration and scoring.
3.1 Query-path planning
For every edge in a query graph, BioThings Explorer consults
the SmartAPI registry for APIs that serve those types of associ-
ations (Fig. 2). For example, in Fig. 1, associations between
diseases and genes can be found using APIs from the
Comparative Toxicogenomics Database (Davis et al. 2023)
and the Biolink API from the Monarch Initiative (Mungall
et al. 2017, Unni et al. 2022), while associations between
genes and chemicals can be found using MyChem.Info
(https://mychem.info/) (Lelong et al. 2022). The sequence of
API calls that can satisfy the original query is a “query-path
plan.”
3.2 Query-path execution
In this phase, BioThings Explorer programmatically and au-
tonomously executes each query in each query-path plan
based on the semantic annotations for each API identified in
the previous phase. BioThings Explorer calls each API, using
the SmartAPI annotation to construct calls with the correct
syntax and appropriate input identifier, and maps the API
responses to the Biolink Model (Unni et al. 2022). BioThings
Explorer also performs ID-to-object translation, which facili-
tates the chaining of API calls from one step in the query-path
to the next step. This ID translation step is critical when suc-
cessive APIs in the query-path plan use different identifiers to
represent the same biomedical entity (e.g. NCBI Gene ID
versus Ensembl Gene ID). ID translation is currently handled
by
the
Translator
Node
Normalizer
(https://github.com/
NCATSTranslator/Translator-All/wiki/Node-Normalizer). The
output of this phase is a set of edges for each step of the query-
path, which represent the associations between biomedical enti-
ties retrieved from the APIs.
3.3 Integration and scoring
In this final phase, these sets of edges from the API queries are
assembled into result sub-graphs, each of which matches the
topology of the query graph. Each result is then scored based
on a variety of factors including number of paths, length of
paths, and semantic similarity between concepts based on the
Normalized Google Distance (Cilibrasi and Vitanyi 2007).
4 Deployment and usage
The BioThings Explorer knowledge graph is entirely com-
posed from a federated network of APIs. Because there is no
local assembly and storage of a large knowledge graph,
BioThings Explorer is a very lightweight application that can
be easily deployed on almost any standard personal com-
puter. The ability of every user to create a local instance of
BioThings Explorer removes centralized bottlenecks associ-
ated with large queries and/or heavy usage. The code reposi-
tory that describes the installation process is at https://github.
com/biothings/biothings_explorer. BioThings Explorer cur-
rently
relies
on
two
external
dependencies—the
Node
Normalizer service and the Biolink Model (Unni et al. 2022).
However, nothing in the BioThings Explorer architecture is
reliant on these specific tools, so these dependencies can be
substituted for alternatives if desired.
For users who prefer not to create a local instance of
BioThings Explorer, we also maintain a community instance
for general use through the NCATS Translator Consortium
(https://explorer.biothings.io/).
2
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5 Discussion
Integration of existing data from multiple disparate sources is
a key step in assessing the state of current knowledge. There
are many existing efforts to create biomedical knowledge
graphs by integrating locally downloaded data and standard-
izing it using a common data model (Himmelstein et al. 2017;
Fecho et al. 2021; Mayers et al. 2022; Wood et al. 2022;
Morris et al. 2023). These efforts result in centralized knowl-
edge graphs of substantial size, often with millions of nodes
and tens of millions of edges.
BioThings Explorer offers a unique strategy for data inte-
gration, focusing on creating a federated knowledge graph by
semantically annotating APIs. Rather than bringing all data
into a massive, centralized graph database, this federated de-
sign instead allows knowledge to remain behind each resour-
ce’s API. Data are retrieved at query time by dynamically
executing API calls and semantically parsing the results. This
architecture
functionally
separates
data
dissemination
(through API creation) from data modeling and data integra-
tion (through semantic annotations).
This approach has several advantages. First, by moving the
requirements for interoperability from implementation in
code to semantic API annotation, we significantly lower the
barrier to participation in our API ecosystem. Second, by sep-
arating these roles into distinct layers, we promote the overall
modularity of our system. These components can develop and
evolve in parallel, and these two roles can even be undertaken
by separate teams (e.g. one team semantically annotates an
API that was created by another team). Third, this design
facilitates
an
iterative
approach
to
API
annotation.
Developers and API annotators can first provide a minimal
set of API metadata, which can later be extended based on fu-
ture needs and use cases.
The federated design of BioThings Explorer also has some
notable
limitations.
First,
our
OpenAPI
extensions
in
SmartAPI to semantically annotate APIs only work on APIs
that follow the REST protocol and provide output in JSON
Figure 1. A visualization of the meta-KG for BioThings Explorer. The nodes in this graph are the semantic types of biomedical entities that BioThings
Explorer can retrieve associations between (limited to the top eight most common semantic types). The edges between nodes show what associations
between biomedical entities exist in the semantic API network that is accessible through BioThings Explorer. The edge label shows the number of APIs
that can retrieve those types of associations, which is also represented by the edge width.
Figure 2. Deconstruction of a query in BioThings Explorer. (A) A free-text
representation of a query that can be answered by BioThings Explorer.
(B) The graph representation of the same query. The exact syntax of this
graph query is specified in the Translator Reasoner API standard
described in Fecho et al. (2022) and shown in Supplementary Fig. S2.
(C) The deconstruction of the graph query into multiple API calls by
consulting the meta-KG in the SmartAPI registry.
BioThings Explorer
3
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format. Second, because the entire federated KG is never in-
stantiated in a single place, reasoning and scoring methods
that rely on having the entire knowledge graph in memory
cannot be used with BioThings Explorer.
In sum, we believe that knowledge graphs enable many ex-
citing use cases in biomedical research (Nicholson and Greene
2020), and that BioThings Explorer is complementary to
existing approaches for assembling knowledge graphs, offer-
ing powerful and unique capabilities for both scientific data
analysts and tool developers.
Supplementary data
Supplementary data are available at Bioinformatics online.
Conflict of interest
None declared.
Funding
Support for this work was provided by the National Center
for Advancing Translational Sciences, National Institutes of
Health, through the Biomedical Data Translator program,
awards OT2TR003427 and OT2TR003445.
Data availability
BioThings Explorer is implemented as a NodeJS application.
The primary repository for the BioThings Explorer project is
at https://github.com/biothings/biothings_explorer, which in
turn links to and incorporates other repositories as sub-
modules. All code is released under the Apache 2.0 open-
source software license.
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End of preview. Expand
in Data Studio
Biosciences RAG Source Documents
Dataset Description
This dataset contains 140 page-level document chunks extracted from 10 biomedical research papers. The documents form the knowledge base for a Retrieval-Augmented Generation (RAG) system focused on biosciences topics including knowledge graphs, LLM applications in biomedicine, and protein interaction databases.
Dataset Summary
- Total Documents: 140 pages from 10 research papers
- Domain: Biomedical NLP, knowledge graphs, RAG in life sciences
- Format: PDF pages with extracted text and metadata
- Use Case: RAG knowledge base for biosciences-related queries
Source Papers
| Pages | Topic |
|---|---|
| 31 | Biomedical preprint (bioRxiv 2025.08.01.668022) |
| 24 | Evaluating LLMs for Gene-to-Phenotype Mapping |
| 18 | Biomedical NLP (arXiv 2405.04819) |
| 16 | A Hybrid LLM-Knowledge Graph Framework for Biomedical QA |
| 12 | Knowledge Graph-Enhanced LLM for Pan-Cancer QA |
| 11 | Biomedical NLP (arXiv 2406.18626) |
| 11 | Improving LLM Applications in Biomedicine with RAG (systematic review) |
| 8 | The STRING Database in 2025: Protein Networks with Directionality |
| 5 | STRING database (Nucleic Acids Research, gkv1277) |
| 4 | Computational biology (btad570) |
Data Fields
page_content(string): Extracted text content from the PDF pagemetadata(dict): Document metadata including:title: Paper titleauthor: Paper authorspage: Page numbertotal_pages: Total pages in source documentsource: Original file pathformat: Document format (PDF)producer,creator: PDF metadatakeywords,subject: Paper keywords and subject (where available)
Data Splits
This dataset contains a single split with all 140 documents.
Licensing
This dataset is released under the Apache 2.0 license.
Dataset Creation
Created as part of the Open Biosciences RAG evaluation pipeline. Source PDFs are ingested via make ingest which runs scripts/ingest_raw_pdfs.py with LlamaIndex transformations (HeadlineSplitter, SummaryExtractor, EmbeddingExtractor, ThemesExtractor, NERExtractor).
Related Datasets
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open-biosciences/biosciences-evaluation-inputs(RAG outputs) - Evaluation Metrics:
open-biosciences/biosciences-evaluation-metrics(RAGAS scores)
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