Learning from Ontology Streams with Semantic Concept Drift

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

*Corresponding author for this work

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

9 Citations (Scopus)

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 publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherAAAI Press / International Joint Conferences on Artificial Intelligence
Pages957-963
Number of pages7
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 2018
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17

Keywords

  • knowledge representation
  • reasoning and logic
  • decription logics and ontologies
  • machine learning
  • time series/data streams

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chen, J., Lécué, F., Pan, J. Z., & Chen, H. (2018). Learning from Ontology Streams with Semantic Concept Drift. In C. Sierra (Ed.), 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 957-963). AAAI Press / International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/133