Collaborative tagging represents for the Web a potential way for organizing and sharing information
and for heightening the capabilities of existing search engines. However, because of the
lack of automatic methodologies for generating the tags and supporting the tagging activity,
many resources on the Web are deficient in tag information, and recommending opportune tags
is both a current open issue and an exciting challenge. This paper approaches the problem by
applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction
methods) thereby generating tags automatically. The proposed approach is implemented
in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework,
a prototype system for personalized content retrieval, annotation, and classification. A case
study application is developed using a domain ontology for software engineering
The concepts of the participative Web, mass collaboration, and collective intelligence grow out of a set of Web methodologies and technologies which improve interaction with users in the development, rating, and distribution of user-generated content. UGC is one of the cornerstones of Web 2.0 and is the core concept of several different kinds of applications. UGC suggests new value chains and business models; it proposes innovative social, cultural, and economic opportunities and impacts. However, several open issues concerning semantic understanding and managing of digital information available on the Web, like information overload, heterogeneity of the available content, and effectiveness of retrieval are still unsolved. The research experiences we present in this chapter, described in literature or achieved in our research laboratory, are aimed at reducing the gap between users and information understanding, by means of collaborative and cognitive filtering, sentiment analysis, information extraction, and knowledge conceptual modeling.
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