Selective Integration of Background Knowledge in TCBR Systems

  • Anil Patelia
  • Sutanu Chakraborti
  • Nirmalie Wiratunga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)


This paper explores how background knowledge from freely available web resources can be utilised for Textual Case Based Reasoning. The work reported here extends the existing Explicit Semantic Analysis approach to representation, where textual content is represented using concepts with correspondence to Wikipedia articles. We present approaches to identify Wikipedia pages that are likely to contribute to the effectiveness of text classification tasks. We also study the effect of modelling semantic similarity between concepts (amounting to Wikipedia articles) empirically. We conclude with the observation that integrating background knowledge from resources like Wikipedia into TCBR tasks holds a lot of promise as it can improve system effectiveness even without elaborate manual knowledge engineering. Significant performance gains are obtained using a very small number of features that have very strong correspondence to how humans describe the domain.


Background Knowledge Semantic Similarity Information Gain Semantic Relatedness Cosine Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anil Patelia
    • 1
  • Sutanu Chakraborti
    • 1
  • Nirmalie Wiratunga
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia
  2. 2.School of ComputingThe Robert Gordon UniversityAberdeenScotland, UK

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