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Traditional techniques of document clustering do not consider the semantic relationships between words\udwhen assigning documents to clusters. For instance, if two documents talking about the same topic do that\udusing different words (which may be synonyms or semantically associated), these techniques may assign\uddocuments to different clusters. Previous research has approached this problem by enriching the document\udrepresentation with the background knowledge in an ontology. This paper presents a new approach to\udenhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map\udterms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of\udterms is calculated by using the Wikipedia's link structure. The document’s vector representation is then\udadjusted so that terms that are semantically related gain more weight. Our approach differs from related\udefforts in two aspects: first, unlink others who built their own methods of measuring similarity through the\udWikipedia categories; our approach uses a similarity measure that is modelled after the Normalized\udGoogle Distance which is a well-known and low-cost method of measuring term similarity. Second, it is\udmore time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms\udwith Wikipedia. Our approach was evaluated by being compared with different methods from the state of\udthe art on two different datasets. Empirical results showed that our approach improved the clustering\udresults as compared to other approaches. \u

An Efficient Approach For Semantically-Enhanced Document Clustering By Using Wikipedia Link Structure