Computing inter-document similarity with Context Semantic Analysis. (February 2019)

• Record Type: Journal Article
• Authors: Benedetti, Fabio; Beneventano, Domenico; Bergamaschi, Sonia; Simonini, Giovanni
• Is Part Of: Information systems. Volume 80(2019)
• Page Start: 136
• Page End: 147
• Publication Date: 2019-02
• Subjects: Knowledge base -- Knowledge graph -- Inter-document similarity -- Similarity measures -- Information Retrieval; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2018.02.009
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature, but CSA differs from them because it is designed to be portable to any RDF knowledge base. In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that: (i) for the general task of inter-document similarity, CSA outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases; (ii) for Information Retrieval tasks, enriching documents with context (i.e., employing the Semantic Context Vector model) improves the results quality of the state-of-the-art technique that employs such similar semantic enrichment .