{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:21:20Z","timestamp":1765232480615},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: \"a ball is used by a football player\", \"a tennis player is located at a tennis court\". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies\u2014specifically, MIT's ConceptNet ontology\u2014can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/178","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"1283-1289","source":"Crossref","is-referenced-by-count":6,"title":["How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval"],"prefix":"10.24963","author":[{"given":"Rodrigo","family":"Toro Icarte","sequence":"first","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge A.","family":"Baier","sequence":"additional","affiliation":[{"name":"Pontificia Universidad Cat\u00f3lica de Chile"},{"name":"Chilean Center for Semantic Web Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristian","family":"Ruz","sequence":"additional","affiliation":[{"name":"Pontificia Universidad Cat\u00f3lica de Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alvaro","family":"Soto","sequence":"additional","affiliation":[{"name":"Pontificia Universidad Cat\u00f3lica de Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:38Z","timestamp":1501228358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/178"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/178","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}