Extracting ontological structures from collaborative tagging systems

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My research focuses on the theoretical and pragmatic issues in integrating the Web 2.0 user-generated content with the Semantic Web technologies. As a popular web 2.0 application, collaborative tagging systems have provided a convenient mechanism that allows online users to collectively annotate and categorize large number of distributed resources from their own diverse perspectives. This kind of informal social classification structures have come to be known as folksonomies. Our ability to search and browse based on folksonomies is limited due to its flat and non-hierarchical structure with unsupervised vocabulary. We propose a novel solution to extract ontological structure from folksonomy and to facilitate its automatic evolution with changing usage patterns, in such a way that the resulting structure can better support semantics-based browsing and searching of online resources. It exploits the power of low support association rule mining supplemented by an upper ontology such as WordNet for the initial extraction and integrates the knowledge of search engine users to help evolve the extracted ontology with the employment of crowdsourcing.