Name
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 em-beddings. The experiments show accurate prediction with data from Dublin and Beijing. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.24963/ijcai.2017/133 | IJCAI |
Field | DocType | Citations |
Data mining,Computer science,Semantic Web,Natural language processing,Artificial intelligence,Semantic computing,Ontology (information science),Ontology-based data integration,Information retrieval,Ontology Inference Layer,Concept drift,Suggested Upper Merged Ontology,Upper ontology | Conference | 3 |
PageRank | References | Authors |
0.37 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J Chen | 1 | 139 | 30.64 |
| Freddy Lécué | 2 | 634 | 50.52 |
| Jeff Z. Pan | 3 | 2218 | 158.01 |
| Huanhuan Chen | 4 | 731 | 101.79 |