Learning from Ontology Streams with Semantic Concept Drift

Jiaoyan Chen, Freddy Lécué, Jeff Z. Pan, Huajun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Pages957-963
Number of pages7
ISBN (Electronic)978-0-9992411-0-3
DOIs
Publication statusPublished - 19 Aug 2017
Event26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
https://ijcai-17.org/index.html
https://ijcai-17.org/
https://ijcai-17.org/

Conference

Conference26th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17
Internet address

Keywords

  • Knowledge Representation, Reasoning, and Logi
  • Description Logics and Ontologies
  • Machine Learning
  • Time-series/Data Streams

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