Giannis Varelas

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Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using(More)
Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity(More)
Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms(More)
Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity(More)
MedSearch is a complete retrieval system for Medline, the premier bibliographic database of the U.S. National Library of Medicine (NLM). MedSearch implements SSRM, a novel information retrieval method for discovering similarities between documents containing semantically similar but not necessarily lexically similar terms.
The recommender systems are new type of software tools designed to help users find their way through today's online shops. Due to the increasing number of e-commerce websites, it is necessary to render effective recommendation to the users. Here we present an overview of current recommendation systems and then our proposed system that employs WordNet(More)
and MeSH terms (MeSH Headings). These descriptions are syntactically analyzed and reduced into separate vectors of MeSH terms which are matched against the queries according to Equation 3 (as similarity between expanded and re-weighted vectors). The weights of all MeSH terms are initialized to one while the weights of titles and abstracts are initialized by(More)
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