Mining Tag Relationships in CQA Sites

Abstract

Community Question Answer (CQA) sites are very popular means for knowledge transfer in the form of questions and answers. They rely on tags to connect the askers with the answerers. Since each CQA site contains information about a wide range of topics, it is difficult for users to navigate through the set of available tags and select the best ones for their question annotation. At present, CQA sites present the tags to the users using simple orderings, such as order by popularity and lexical order. This paper proposes a novel unsupervised method to mine different types of relationships between tags and then create a forest of ontologies to representing those relationships. Extracting the tag relationships will help users to understand the tags meanings. Representing them in a forest of ontologies will help the users in better tag navigation, thereby providing the users a clear understanding of the tag usage for question annotation. Moreover, our method can also be combined with existing tag recommendation systems to improve them. We evaluate our tag relationship mining algorithms and tag ontology construction algorithm with the state-of-the-art baseline methods and the three popular knowledge bases, namely DBpedia, ConceptNet, and WebIsAGraph.