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.
|Title of host publication||26th International Joint Conference on Artificial Intelligence, IJCAI 2017|
|Publisher||AAAI Press / International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 2018|
|Event||26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia|
Duration: 19 Aug 2017 → 25 Aug 2017
|Conference||26th International Joint Conference on Artificial Intelligence, IJCAI 2017|
|Period||19/08/17 → 25/08/17|
Bibliographical noteThis work is funded by NSFC 61473260/61673338/61672393, the Alibaba-ZJU joint project on e-Business Knowledge Graph, and the EU Marie Curie IAPP K-Drive project (286348).
- knowledge representation
- reasoning and logic
- decription logics and ontologies
- machine learning
- time series/data streams