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

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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.
OriginalspracheEnglisch
TitelECAI 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
HerausgeberUlle Endriss, Francisco S. Melo, Kerstin Bach, Alberto José Bugarín Diz, Jose Maria Alonso-Moral, Senén Barro, Fredrik Heintz
Anzahl der Seiten8
ErscheinungsortAmsterdam
VerlagIOS Press BV
Erscheinungsdatum2024
Seiten1198-1205
ISBN (elektronisch)978-1-64368-548-9
DOIs
PublikationsstatusErschienen - 2024
Veranstaltung27th European Conference on Artificial Intelligence - ECAI 2024: "Celebrating the past. Inspiring the future" - University of Santiago de Compostela., Santiago de Compostela, Spanien
Dauer: 19.10.202424.10.2024
Konferenznummer: 27
https://www.ecai2024.eu/

DOI