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Various applications in the areas of computational linguistics and artificialintelligence employ semantic similarity to solve challenging tasks, such asword sense disambiguation, text classification, information retrieval, machinetranslation, and document clustering. Previous work on semantic similarityfollowed a mono-relational approach using mostly the taxonomic relation "ISA".This paper explores the benefits of using all types of non-taxonomic relationsin large linked data, such as WordNet knowledge graph, to enhance existingsemantic similarity and relatedness measures. We propose a holisticpoly-relational approach based on a new relation-based information content andnon-taxonomic-based weighted paths to devise a comprehensive semanticsimilarity and relatedness measure. To demonstrate the benefits of exploitingnon-taxonomic relations in a knowledge graph, we used three strategies todeploy non-taxonomic relations at different granularity levels. We conductedexperiments on four well-known gold standard datasets, and the resultsdemonstrated the robustness and scalability of the proposed semantic similarityand relatedness measure, which significantly improves existing similaritymeasures.

Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet