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Measuring Semantic Relatedness using Wikipedia Signed Network

Identifying the semantic relatedness of two words is an important task for the in- formation retrieval, natural language processing, and text mining. However, due to the diversity of meaning for a word, the semantic relatedness of two words is still hard to precisely evaluate under the limited corpora. Nowadays, Wikipedia is now a huge and wiki-based encyclopedia on the internet that has become a valuable resource for research work. Wikipedia articles, written by a live collaboration of user editors, contain a high volume of reference links, URL identification for concepts and a complete revision his- tory. Moreover, each Wikipedia article represents an individual concept that simultane- ously contains other concepts that are hyperlinks of other articles embedded in its con- tent. Through this, we believe that the semantic relatedness between two words can be found through the semantic relatedness between two Wikipedia articles. Therefore, we propose an Editor-Contribution-based Rank (ECR) algorithm for ranking the concepts in the article's content through all revisions and take the ranked concepts as a vector repre- senting the article. We classify four types of relationship in which the behavior of addi- tion and deletion maps appropriate and inappropriate concepts. ECR also extend the concept semantics by the editor-concept network. ECR ranks those concepts depending on the mutual signed-reinforcement relationship between the concepts and the editors. The results reveal that our method leads to prominent performance improvement and in- creases the correlation coefficient by a factor ranging from 4% to 23% over previous methods that calculate the relatedness between two articles.