We propose a novel knowledge-based technique for interdocument similarity, 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. Our technique relies on a generic RDF knowledge base (e.g. DBpedia andWikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods.
CITATION STYLE
Benedetti, F., Beneventano, D., & Bergamaschi, S. (2016). Context semantic analysis: A knowledge-based technique for computing inter-document similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9939 LNCS, pp. 164–178). Springer Verlag. https://doi.org/10.1007/978-3-319-46759-7_13
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