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Conclusions and Future Work

  • Yi Cai
  • Ching-man Au Yeung
  • Ho-fung Leung

Abstract

In this book, we propose two new formal models of ontology with object membership and object typicality, a first model and a better model. These models are based on the theories in cognitive psychology and fuzzy set theory. They extend current ontologies to reflect object typicality and object membership in concepts. Besides, we formalize object typicality in context-aware ontologies based on property importance. In addition, in order to handle concepts defined by property importance and property priority, we extend our better model to handle object membership in concepts with weighted properties and prioritized properties. Based on the idea of object typicality from cognitive psychology, we study recommendation systems from new perspectives and propose some novel recommendation approaches.

Keywords

Recommendation System Knowledge Representation Future Research Direction Object Typicality Weighted Property 
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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References

  1. [1]
    Helsper EM, van der Gaag LC, Feelders AJ et al (2005) Bringing Order into Bayesian-network Construction. In: Proceedings of Third International Conference on Knowledge Capture.Google Scholar
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    Smith EE, Medin DL (1981) Categories and Concepts. Harvard University Press, Boston.Google Scholar
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    Cohen B, Murphy GL (1984) Models of Concepts. Cognitive Science 8: 27–58.CrossRefGoogle Scholar

Copyright information

© Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yi Cai
    • 1
  • Ching-man Au Yeung
    • 2
  • Ho-fung Leung
    • 3
  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Hong Kong Applied Science and Technology Research InstituteHong KongChina
  3. 3.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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