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Integrating Concepts and Knowledge in Large Content Networks

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Abstract

Large content networks like the World Wide Web contain huge amounts of information that have the potential of being integrated because their components fit within common concepts and/or are connected through hidden, implicit relationships. One attempt at such an integration is the program called the “Web of Data,” which is an evolution of the Semantic Web. It targets semi-structured information sources such as Wikipedia and turns them into fully structured ones in the form of Web-based databases like DBpedia and then integrates them with other public databases such as Geonames. On the other hand, the vast majority of the information residing on the Web is still totally unstructured, which is the starting point for our approach that aims to integrate unstructured information sources. For this purpose, we exploit techniques from Probabilistic Topic Modeling, in order to cluster Web pages into concepts (topics), which are then related through higher-level concept networks; we also make implicit semantic relationships emerge between single Web pages. The approach has been tested through a number of case studies that are here described. While the applicative focus of the research reported here is on knowledge integration on the specific and relevant case of the WWW, the wider aim is to provide a framework for integration generally applicable to all complex content networks where information propagates from multiple sources.

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Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano - Bicocca, Milan, Italy

    Marco Rossetti, Fabio Stella & Francesca Arcelli fontana

  2. Department of Bioscience and Territory, University of Molise, Campobasso, Italy

    Remo Pareschi

Authors
  1. Marco Rossetti
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  2. Remo Pareschi
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  3. Fabio Stella
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  4. Francesca Arcelli fontana
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Correspondence to Marco Rossetti.

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Rossetti, M., Pareschi, R., Stella, F. et al. Integrating Concepts and Knowledge in Large Content Networks. New Gener. Comput. 32, 309–330 (2014). https://doi.org/10.1007/s00354-014-0407-4

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  • DOI: https://doi.org/10.1007/s00354-014-0407-4

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