{"type":"ScholarlyArticle","title":"Learning from Ontology Streams with Semantic Concept Drift","authors":[{"name":"Jiaoyan Chen","slug":"jiaoyan-chen","given":"Jiaoyan","family":"Chen","affiliation":"Zhejiang University China"},{"name":"Freddy Lecue","slug":"freddy-lecue","given":"Freddy","family":"Lecue","affiliation":"INRIA , France; Accenture Labs , Ireland"},{"name":"Jeff Z. Pan","slug":"jeff-z-pan","given":"Jeff Z.","family":"Pan","affiliation":"University of Aberdeen United Kingdom"},{"name":"Huajun Chen","slug":"huajun-chen","given":"Huajun","family":"Chen","affiliation":"Zhejiang University China"}],"abstract":"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.","doi":"10.24963/ijcai.2017/133","url":"https://ijcai.org/proceedings/2017/133","pdf":"https://ijcai.org/proceedings/2017/0133.pdf","markdown":"https://ijcai.org/proceedings/2017/133.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/133","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":133,"pages":{"first":957,"last":963},"keywords":["Machine Learning: Time-series/Data Streams","Knowledge Representation, Reasoning, and Logic: Description Logics and Ontologies"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 1996] Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. 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