A General Framework for Personalized Text Classification and Annotation

Abstract. The tremendous volume of digital contents available today on the Web and the rapid spread of Web 2.0 sites, blogs and forums have exacerbated the classical information overload problem. Moreover, they have made even worse the challenge of finding new content appropriate to individual needs. In order to alleviate these issues, new approaches and tools are needed to provide personalized content recommendations and classification schemata. This paper presents the PIRATES framework: a Personalized Intelligent Recommender and Annotator TEStbed for text-based content retrieval and classification. Using an integrated set of tools, this framework lets the users experiment, customize, and personalize the way they retrieve, filter, and organize the large amount of information available on the Web. Furthermore, the PIRATES framework undertakes a novel approach that automates typical manual tasks such as content annotation and tagging, by means of personalized tags recommendations and other forms of textual annotations (e.g. key-phrases).

Accessing, analysing, and extracting information from user generated contents: open issues and challenges

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

Automatic keyphrase extraction and ontology mining for content-based tag recommendation

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

Generating and sharing personal information spaces

Applications based on the Web 2.0 approach show several limitations: among them, knowledge is usually manually generated by users and can not be structured and shared in effective ways. This paper presents an innovative architecture, conceived in terms of a multi-agent systems and aimed at creating, managing and sharing personal information spaces. Data and knowledge may be directly added by users, but also collected and structured with the support of content retrieval, filtering and automatic tagging techniques. Conceptual spaces organize personal information spaces using zz-structures, an innovative system of conventions for data and computing, capable of representing, by means graph-centric views, contextual interconnections among heterogeneous information