Inducing Ontologies from Folksonomies using Natural Language Understanding

Abstract

Folksonomies are unsystematic, unsophisticated collections of keywords associated by social bookmarking users to web content and, despite their inconsistency problems (typographical errors, spelling variations, use of space or punctuation as delimiters, same tag applied in different context, synonymy of concepts, etc.), their popularity is increasing among Web 2.0 application developers. In this paper, in addition to eliminating folksonomic irregularities existing at the lexical, syntactic or semantic understanding levels, we propose an algorithm that automatically builds a semantic representation of the folksonomy by exploiting the tags, their social bookmarking associations (co-occuring tags) and, more importantly, the content of labeled documents. We derive the semantics of each tag, discover semantic links between the folksonomic tags and expose the underlying semantic structure of the folksonomy, thus, enabling a number of information discovery and ontology-based reasoning applications.

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