Talking to GDELT Through Knowledge Graphs
Paper • 2503.07584 • Published
page_content stringlengths 10 9.29k | metadata dict |
|---|---|
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... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 ... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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-l... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 disco... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 knowl... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 th... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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
Uploa... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 Cy... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 (su... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 res... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 appea... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 spe... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 tre... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
Omar & Mohammed / Journal of Applied Science and Technology Trends Vol. 06, No. 02, pp. 342 –357 (2025)
357
[4] Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto,
and P. Fung, “Survey of hallucination in natural language generation,”
ACM Computing Surveys, vol. 55, no. 12, pp. 1–38,... | {
"author": "",
"creationDate": "D:20251020163736+03'00'",
"creationdate": "2025-10-20T16:37:36+03:00",
"creator": "Microsoft® Word for Microsoft 365",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/JASTTytm_A+Hybrid+LLM–Knowledge+Graph_.pdf",
"format": "PDF 1.7",
"keywords": "",
"modDate": "D... |
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 Augme... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 qual... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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-slic... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
… 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 Trip... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 e... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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
... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 mod... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 ->... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 samplin... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
Geesa Daluwatumulle, Rupika Wijesinghe, and Ruvan
Weerasinghe. 2023. In silico drug repurposing us-
ing knowledge graph embeddings for alzheimer’s
disease. In Proceedings of the 9th International Con-
ference on Bioinformatics Research and Applications,
ICBRA ’22, page 61–66, New York, NY, USA. As-
sociation for Comput... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
of the 2023 Conference on Empirical Methods in
Natural Language Processing, pages 6342–6353.
Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang,
and Nigam Shah. 2023. Clinfo. ai: An open-source
retrieval-augmented large language model system for
answering medical questions using scientific litera-
ture. In PACIFIC SYM... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng
Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh
Karami, Jundong Li, Lu Cheng, and Huan Liu. 2024.
Large language models for data annotation: A survey.
arXiv preprint arXiv:2402.13446.
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su,
Suqi Cheng, Dawei Yin, and Chao ... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song,
and Cane Wing-Ki Leung. 2020. Aser: A large-scale
eventuality knowledge graph. In Proceedings of the
web conference 2020, pages 201–211.
Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Ad-
hikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin
Zhou, Xiang Li, et al. 2023a. ... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 ... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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) pro... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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 knowled... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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.a... | {
"author": "",
"creationDate": "D:20240509001837Z",
"creationdate": "2024-05-09T00:18:37+00:00",
"creator": "LaTeX with hyperref",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/2405.04819v1.pdf",
"format": "PDF 1.5",
"keywords": "",
"modDate": "D:20240509001837Z",
"moddate": "2024-05-09T00... |
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,... | {
"author": "",
"creationDate": "D:20230927155028+05'30'",
"creationdate": "2023-09-27T15:50:28+05:30",
"creator": "Arbortext Advanced Print Publisher 9.0.114/W",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/btad570.pdf",
"format": "PDF 1.3",
"keywords": "",
"modDate": "D:20260111011054+00'0... |
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. Open... | {
"author": "",
"creationDate": "D:20230927155028+05'30'",
"creationdate": "2023-09-27T15:50:28+05:30",
"creator": "Arbortext Advanced Print Publisher 9.0.114/W",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/btad570.pdf",
"format": "PDF 1.3",
"keywords": "",
"modDate": "D:20260111011054+00'0... |
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;
Fec... | {
"author": "",
"creationDate": "D:20230927155028+05'30'",
"creationdate": "2023-09-27T15:50:28+05:30",
"creator": "Arbortext Advanced Print Publisher 9.0.114/W",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/btad570.pdf",
"format": "PDF 1.3",
"keywords": "",
"modDate": "D:20260111011054+00'0... |
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 (Nich... | {
"author": "",
"creationDate": "D:20230927155028+05'30'",
"creationdate": "2023-09-27T15:50:28+05:30",
"creator": "Arbortext Advanced Print Publisher 9.0.114/W",
"file_path": "/home/donbr/ag-discovery/gdelt-kb/data/raw/btad570.pdf",
"format": "PDF 1.3",
"keywords": "",
"modDate": "D:20260111011054+00'0... |
End of preview. Expand in Data Studio
GDELT RAG Source Documents
Dataset Description
This dataset contains source documents extracted from the research paper "Talking to GDELT Through Knowledge Graphs" (arXiv:2503.07584v3). The documents are used as the knowledge base for a Retrieval-Augmented Generation (RAG) system focused on GDELT (Global Database of Events, Language, and Tone) analysis.
Dataset Summary
- Total Documents: 38 pages
- Source: Research paper on GDELT Knowledge Graphs
- Format: PDF pages with extracted text and metadata
- Use Case: RAG knowledge base for GDELT-related queries
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 documentfile_path: Original file pathformat: Document format (PDF)producer,creator: PDF metadata- Other PDF metadata fields
Data Splits
This dataset contains a single split with all 38 documents.
Source Data
The source material is the research paper:
- Title: "Talking to GDELT Through Knowledge Graphs"
- Authors: Audun Myers, Max Vargas, Sinan G. Aksoy, Cliff Joslyn, Benjamin Wilson, Lee Burke, Tom Grimes
- arXiv ID: 2503.07584v3
Licensing
This dataset is released under the Apache 2.0 license.
Citation
If you use this dataset, please cite the original paper:
@article{myers2025talking,
title={Talking to GDELT Through Knowledge Graphs},
author={Myers, Audun and Vargas, Max and Aksoy, Sinan G and Joslyn, Cliff and Wilson, Benjamin and Burke, Lee and Grimes, Tom},
journal={arXiv preprint arXiv:2503.07584},
year={2025}
}
Dataset Creation
This dataset was created as part of the AI Engineering Bootcamp Cohort 8 certification challenge project.
- Downloads last month
- 15