Learning from Ontology Streams with Semantic Concept Drift

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

IJCAI 2017 pp. 957-963

doi:10.24963/IJCAI.2017/133 /ijcai/2017/chen2017ijcai-learning/

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.

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Cite

Text

Chen et al. "Learning from Ontology Streams with Semantic Concept Drift." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/133

Markdown

[Chen et al. "Learning from Ontology Streams with Semantic Concept Drift." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/chen2017ijcai-learning/) doi:10.24963/IJCAI.2017/133

BibTeX

@inproceedings{chen2017ijcai-learning,
  title     = {{Learning from Ontology Streams with Semantic Concept Drift}},
  author    = {Chen, Jiaoyan and Lécué, Freddy and Pan, Jeff Z. and Chen, Huajun},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {957-963},
  doi       = {10.24963/IJCAI.2017/133},
  url       = {https://mlanthology.org/ijcai/2017/chen2017ijcai-learning/}
}