{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:30:23Z","timestamp":1761611423810},"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>Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/133","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"957-963","source":"Crossref","is-referenced-by-count":13,"title":["Learning from Ontology Streams with Semantic Concept Drift"],"prefix":"10.24963","author":[{"given":"Jiaoyan","family":"Chen","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Freddy","family":"Lecue","sequence":"additional","affiliation":[{"name":"INRIA, Sophia Antipolis, France"},{"name":"Accenture Labs, Dublin, Ireland"}]},{"given":"Jeff Z.","family":"Pan","sequence":"additional","affiliation":[{"name":"University of Aberdeen, Aberdeen, United Kingdom"}]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]}],"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":"2017-07-28T07:52:24Z","timestamp":1501228344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/133"}},"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\/133","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}