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Sub-Symbolic Knowledge Representation for Evocative Chat-Bots

  • G. Pilato
  • A. Augello
  • G. Vassallo
  • S. Gaglio

Abstract

A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with “evocative” capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper.

Keywords

Latent Semantic Analysis Semantic Space Ontology Concept Conversational Agent Lexical Category 
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.
    Pilato, G., Augello, A., Trecarichi, G., Vassallo, G., & Gaglio, S. (2005). LSA-Enhanced On-tologies for Information Exploration System on Cultural Heritage. AIIA Workshop for Cultural Heritage. University of Milan Bicocca, Milano, Italy Google Scholar
  2. 2.
  3. 3.
    Goh, O. S., Ardil, C., Wong, W., & Fung, C. C. (2006). A Black-Box Approach for Response Quality Evaluation Conversational Agent System. International Journal of Computational In-telligence. Vol. 3, 195-203 Google Scholar
  4. 4.
    Landauer, T. K., Foltz, P. W., & Laham, D. (1998). Introduction to Latent Semantic Analysis. Discourse Processes. Vol. 25, 259-284 CrossRefGoogle Scholar
  5. 5.
    Miller, G. A., Beckwidth, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990). Introduction to WordNet: An On-line Lexical Database. International Journal of Lexicography. Vol. 3 N. 4, 235-244 CrossRefGoogle Scholar
  6. 6.
    Patwardhan, S. & Pedersen, T. (2006). Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together. Trento, Italy, pp. 1-8Google Scholar
  7. 7.
    Agostaro, F., Pilato, G., Vassallo, G., & Gaglio, S. (2005). A Subsymbolic Approach to Word Modelling for Domain Specific Speech Recognition. Proceedings of IEEE CAMP05 In-ternational Workshop on Computer Architecture for Machine Perception. Terrasini-Palermo, July 4-6, pp. 321-326Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • G. Pilato
    • 1
  • A. Augello
    • 2
  • G. Vassallo
    • 2
  • S. Gaglio
    • 1
    • 2
  1. 1.CNR - ICARPalermoItaly
  2. 2.DINFO, Dipartimento di Ingegneria InformaticaUniversità degli Studi di PalermoPalermoItaly

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