Computing inter-document similarity with Context Semantic Analysis

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. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a contextual graph and a semantic contextual vector able to represent the context of a document. We show how CSA exploits such Semantic Context Vector to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases.