Title
Learning from Ontology Streams with Semantic Concept Drift.
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.
Year
Venue
DocType
2017
CoRR
Journal
Volume
Citations 
PageRank 
abs/1704.07466
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Freddy Lécué163450.52
J Chen213930.64
Jeff Z. Pan32218158.01
Huanhuan Chen4731101.79