{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:57:50Z","timestamp":1784044670835,"version":"3.55.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>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 \u2013 PrimeKGQA \u2013 serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.<\/jats:p>","DOI":"10.3233\/faia240615","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:56:16Z","timestamp":1729169776000},"source":"Crossref","is-referenced-by-count":3,"title":["Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1829-6554","authenticated-orcid":false,"given":"Xi","family":"Yan","sequence":"first","affiliation":[{"name":"Universit\u00e4t Hamburg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-4485","authenticated-orcid":false,"given":"Patrick","family":"Westphal","sequence":"additional","affiliation":[{"name":"Universit\u00e4t Hamburg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1337-2850","authenticated-orcid":false,"given":"Jan","family":"Seliger","sequence":"additional","affiliation":[{"name":"Universit\u00e4tsklinikum Hamburg-Eppendorf"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0191-7211","authenticated-orcid":false,"given":"Ricardo","family":"Usbeck","sequence":"additional","affiliation":[{"name":"Leuphana Universit\u00e4t L\u00fcneburg"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:56:17Z","timestamp":1729169777000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240615","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}