Text mining through semi automatic semantic annotation

  • Authors:

  • Nadzeya Kiyavitskaya;Nicola Zeni;Luisa Mich;James R. Cordy;John Mylopoulos

  • Affiliations:

  • Dept. of Information and Communication Technology, University of Trento, Italy;Dept. of Information and Communication Technology, University of Trento, Italy;Dept. of Computer and Management Sciences, University of Trento, Italy;School of Computing, Queens University, Kingston, Canada;Dept. of Computer Science, University of Toronto, Ontario, Canada

  • Venue:
  • PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management
  • Year:
  • 2006

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Abstract

The Web is the greatest information source in human history. Unfortunately, mining knowledge out of this source is a laborious and error-prone task. Many researchers believe that a solution to the problem can be founded on semantic annotations that need to be inserted in web-based documents and guide information extraction and knowledge mining. In this paper, we further elaborate a tool-supported process for semantic annotation of documents based on techniques and technologies traditionally used in software analysis and reverse engineering for large-scale legacy code bases. The outcomes of the paper include an experimental evaluation framework and empirical results based on two case studies adopted from the Tourism sector. The conclusions suggest that our approach can facilitate the semi-automatic annotation of large document bases.