Ranking semantic associations between two entities --- extended model

  • Authors:

  • V. Viswanathan;Ilango Krishnamurthi

  • Affiliations:

  • Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India;Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India

  • Venue:
  • ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
  • Year:
  • 2012

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Abstract

Semantic association is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. The number of relationships between entities in a knowledge base might be much greater than the number of entities. So, ranking the relationship paths is required to find the relevant relationships with respect to the user's domain of interest. In some situations, user may expect the semantic relationships with respect to specific domain closer to any one of these entities. Consider the example for finding the semantic association between the person X and person Y. If the user has already known something about the person X such as person X may be associated with financial activities or scientific research etc., then the user wants to focus on finding and ranking the relationship between two persons in which the users' context is closer to person X. In many of the existing systems, there is no consideration given into context closeness during ranking process. In this paper, we present an approach which allows the extraction of semantic associations between two entities depending on the choice of the user in which the context is closer to left or right entity. The average correlation coefficient between proposed ranking and human ranking is 0.70. We compare the results of our proposed method with other existing methods. It explains that the proposed ranking is highly correlated with human ranking. According to our experiments, the proposed system provides the highest precision rate in ranking the semantic association paths.