Apport des ontologies pour le calcul de la similarité sémantique au sein d'un système de recommandation
This work addresses semantic similarity calculation for recommender systems, but appears incremental as it builds on existing ontology methods.
The paper tackles the problem of measuring semantic similarity in recommender systems by proposing an ontology-based approach, but does not provide concrete numerical results.
Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The calculation of semantic similarity from ontology has developed and depending on the context is complemented by other similarity calculation methods. In this paper, we propose and carry on an approach for the calculation of ontology-based semantic similarity using in the context of a recommender system.