Browsing by Subject "text mining"
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Item Feeds That Matter: A Study of Bloglines Subscriptions(2007-03-25) Java, Akshay; Kolari, Pranam; Finin, Tim; Joshi, Anupam; Oates, TimDavid Sifry's latest quarterly report on the state of the Blogosphere states that ``the size of the Blogosphere continues to double every six months". According to this report there are 33.5 million weblogs and many of these are actively posting. As the Blogosphere continues to grow, finding good quality feeds is becoming increasingly difficult. In this paper we present an analysis of the feeds subscribed by a set of publicly listed Bloglines users. Using the subscription information, we describe techniques to induce an intuitive set of topics for feeds and blogs. These topic categories, and their associated feeds, are key to a number of blog-related applications, including the compilation of a list of feeds that matter for a given topic. The site FTM! (Feeds That Matter) was implemented to help users browse and subscribe to an automatically generated catalog of popular feeds for different topics.Item Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies(2004-06-21) Parekh, Viral; Gwo, Jack; Finin, TimOntologies have been widely accepted as the most advanced knowledge representation model. They are among the most important building blocks of semantic web, hence, very crucial for the success of semantic web. This paper discusses a fast and efficient method to facilitate the evaluation and enrichment of domain ontologies using a text-mining approach. We exploit domain specific texts and glossaries or dictionaries in order to automatically generate g-groups and f-groups. These groups are sets of concepts/terms which have either taxonomic or non-taxonomic relationships among them. The domain expert ontology engineer reviews these generated groups and uses them to evaluate and enrich the domain ontology. We have developed an extensive and detailed ontology in the field of environmental science using this approach in interaction with domain expert. Empirical results show that our approach can support domain expert ontology engineers in building domain specific ontologies efficiently.