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Finding relevant semantic association paths using semantic ant colony optimization algorithm

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Abstract

Semantic Associations are complex relationships between entities in a knowledge base represented in a graph. While searching Semantic Association between entities in an RDF graph, there may be too many paths connecting them. Each path has different meaning depending on the type of relationships, in which, some of them may be irrelevant according to the users’ perspective and these paths are to be filtered. To improve the relevance in finding semantic association, the proposed research suggests Semantic Ant Colony Optimization algorithm in searching paths between entities in an RDF graph. Experiments are conducted to analyze the efficiency of the algorithm in searching the relevant paths and to check for the quality of solution. The results show that the proposed approach provide more relevant semantic associations according to the users’ perspective.

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Authors and Affiliations

  1. School of Computing Sciences and Engineering, VIT University, Chennai Campus, Chennai, 600127, Tamil Nadu, India

    V. Viswanathan

  2. Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil nadu, India

    Ilango Krishnamurthi

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  1. V. Viswanathan
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  2. Ilango Krishnamurthi
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Correspondence to V. Viswanathan.

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Communicated by G. Acampora.

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Viswanathan, V., Krishnamurthi, I. Finding relevant semantic association paths using semantic ant colony optimization algorithm. Soft Comput 19, 251–260 (2015). https://doi.org/10.1007/s00500-014-1247-3

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