Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/eswc2008/paper/273> ?p ?o. }
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- 273 creator claudia-damato.
- 273 creator floriana-esposito.
- 273 creator nicola-fanizzi.
- 273 type InProceedings.
- 273 label "Conceptual Clustering and its Application to Concept Drift and Novelty Detection".
- 273 sameAs 273.
- 273 abstract "We present a method based on clustering techniques to detect concept drift or novelty in a knowledge based expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). A maximally discriminating group of features can be obtained with the randomized optimization methods described in the paper. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language. We propose a method for exploiting the clustering results for concept drift and novelty detection".
- 273 hasAuthorList authorList.
- 273 hasTopic Machine_learning.
- 273 hasTopic Ontology_%28computer_science%29.
- 273 hasTopic Semantic_Web.
- 273 hasTopic Web_Ontology_Language.
- 273 isPartOf proceedings.
- 273 keyword "concept drift".
- 273 keyword "conceptual clustering".
- 273 keyword "novelty setection".
- 273 keyword "semantic similarity".
- 273 title "Conceptual Clustering and its Application to Concept Drift and Novelty Detection".