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Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>Factoid entity questions (FEQ), which seek answers in the form of a single entity from knowledge sources, such as DBpedia and Wikidata, constitute a substantial portion of user queries in search engines. This article introduces the knowledge graph embedding model for FEQ (KGE-FEQ) answering. Leveraging a textual knowledge graph derived from extensive text collections, KGE-FEQ encodes textual relationships between entities. The model employs a two-step process: (1) Triple Retrieval, where relevant triples are retrieved from the textual knowledge graph based on semantic similarities to the question, and (2) Answer Selection, where a knowledge graph embedding approach is utilized for answering the question. This involves positioning the embedding for the answer entity close to the embedding of the question entity, incorporating a vector representing the question and textual relations between entities. Extensive experiments evaluate the performance of the proposed approach, comparing KGE-FEQ to state-of-the-art baselines in FEQ answering and the most advanced open-domain question answering techniques applied to FEQs. The results show that KGE-FEQ outperforms existing methods across different datasets. Ablation studies highlights the effectiveness of KGE-FEQ when both the question and textual relations between entities are considered for answering questions.<\/jats:p>","DOI":"10.1145\/3678003","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:41:55Z","timestamp":1721043715000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["A Knowledge Graph Embedding Model for Answering Factoid Entity Questions"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7289-7082","authenticated-orcid":false,"given":"Parastoo","family":"Jafarzadeh","sequence":"first","affiliation":[{"name":"Toronto Metropolitan University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8891-0249","authenticated-orcid":false,"given":"Faezeh","family":"Ensan","sequence":"additional","affiliation":[{"name":"Toronto Metropolitan University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0151-9922","authenticated-orcid":false,"given":"Mahdiyar","family":"Ali Akbar Alavi","sequence":"additional","affiliation":[{"name":"Toronto Metropolitan University, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2102-9190","authenticated-orcid":false,"given":"Fattane","family":"Zarrinkalam","sequence":"additional","affiliation":[{"name":"University of Guelph, Guelph, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591698"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591907"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1160"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103242"},{"key":"e_1_3_2_6_2","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. 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