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Semantic Network Formalism for Knowledge Representation: Towards Consideration of Contextual Information

Author

Listed:
  • Souheyl Mallat

    (LATICE Laboratory Research Department of Computer Science, Tunisia, University of Monastir, Monastir, Tunisia)

  • Emna Hkiri

    (LATICE Laboratory Research Department of Computer Science, Tunisia, University of Monastir, Monastir, Tunisia)

  • Mohsen Maraoui

    (Computational Mathematics Laboratory, University of Monastir, Monastir, Tunisia)

  • Mounir Zrigui

    (LATICE Laboratory Research Department of Computer Science, Tunisia, University of Monastir, Monastir, Tunisia)

Abstract

In this paper, the authors propose formalism for representing a knowledge base (KB) by network. The objective is to achieve a high coverage of this base. This type of network is similar to the semantic network with the difference that the arcs are quantified by a value indicating the semantic proximity between the concepts. This semantic proximity presents taxonomic relations, synonyms, and non-taxonomic relations (contextual relations). This latter are discovered based on the association rules model. This model is based on (i) indexing method (ii) the French lexical database EuroWordNet (EWNF) and (iii) the Apriori algorithm. The contextual relations are the latent relations buried in the KB, carried by the semantic context. Evaluating our representation formalism shows better result about 80% of coverage of the KB.

Suggested Citation

  • Souheyl Mallat & Emna Hkiri & Mohsen Maraoui & Mounir Zrigui, 2015. "Semantic Network Formalism for Knowledge Representation: Towards Consideration of Contextual Information," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 11(4), pages 64-85, October.
    • Handle: RePEc:igg:jswis0:v:11:y:2015:i:4:p:64-85
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