Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset. / Yan, Xi; Westphal, Patrick; Seliger, Jan et al.
ECAI 2024 : 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain; including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings. Hrsg. / Ulle Endriss; Francisco S. Melo; Kerstin Bach; Alberto José Bugarín Diz; Jose Maria Alonso-Moral; Senén Barro; Fredrik Heintz. Amsterdam: IOS Press BV, 2024. S. 1198-1205 (Frontiers in Artificial Intelligence and Applications; Band 392).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Yan, X, Westphal, P, Seliger, J & Usbeck, R 2024, Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset. in U Endriss, FS Melo, K Bach, AJB Diz, JM Alonso-Moral, S Barro & F Heintz (Hrsg.), ECAI 2024 : 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain; including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings. Frontiers in Artificial Intelligence and Applications, Bd. 392, IOS Press BV, Amsterdam, S. 1198-1205, 27th European Conference on Artificial Intelligence - ECAI 2024, Santiago de Compostela, Spanien, 19.10.24. https://doi.org/10.3233/FAIA240615

APA

Yan, X., Westphal, P., Seliger, J., & Usbeck, R. (2024). Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset. In U. Endriss, F. S. Melo, K. Bach, A. J. B. Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Hrsg.), ECAI 2024 : 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain; including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings (S. 1198-1205). (Frontiers in Artificial Intelligence and Applications; Band 392). IOS Press BV. https://doi.org/10.3233/FAIA240615

Vancouver

Yan X, Westphal P, Seliger J, Usbeck R. Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset. in Endriss U, Melo FS, Bach K, Diz AJB, Alonso-Moral JM, Barro S, Heintz F, Hrsg., ECAI 2024 : 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain; including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings. Amsterdam: IOS Press BV. 2024. S. 1198-1205. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA240615

Bibtex

@inbook{41d62101511041df813ac0c8f77d9b15,
title = "Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset",
abstract = "Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 85,368 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.",
keywords = "Business informatics",
author = "Xi Yan and Patrick Westphal and Jan Seliger and Ricardo Usbeck",
note = "{\textcopyright} 2024 The Authors.; 27th European Conference on Artificial Intelligence - ECAI 2024 : {"}Celebrating the past. Inspiring the future{"}, ECAI 2024 ; Conference date: 19-10-2024 Through 24-10-2024",
year = "2024",
doi = "10.3233/FAIA240615",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "1198--1205",
editor = "Ulle Endriss and Melo, {Francisco S.} and Kerstin Bach and Diz, {Alberto Jos{\'e} Bugar{\'i}n} and Alonso-Moral, {Jose Maria} and Sen{\'e}n Barro and Fredrik Heintz",
booktitle = "ECAI 2024",
address = "Netherlands",
url = "https://www.ecai2024.eu/",

}

RIS

TY - CHAP

T1 - Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset

AU - Yan, Xi

AU - Westphal, Patrick

AU - Seliger, Jan

AU - Usbeck, Ricardo

N1 - Conference code: 27

PY - 2024

Y1 - 2024

N2 - Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 85,368 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.

AB - Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 85,368 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.

KW - Business informatics

U2 - 10.3233/FAIA240615

DO - 10.3233/FAIA240615

M3 - Article in conference proceedings

T3 - Frontiers in Artificial Intelligence and Applications

SP - 1198

EP - 1205

BT - ECAI 2024

A2 - Endriss, Ulle

A2 - Melo, Francisco S.

A2 - Bach, Kerstin

A2 - Diz, Alberto José Bugarín

A2 - Alonso-Moral, Jose Maria

A2 - Barro, Senén

A2 - Heintz, Fredrik

PB - IOS Press BV

CY - Amsterdam

T2 - 27th European Conference on Artificial Intelligence - ECAI 2024

Y2 - 19 October 2024 through 24 October 2024

ER -