Minds and Machines

, Volume 23, Issue 1, pp 105–121

An Ontology-Based Approach to Metaphor Cognitive Computation

  • Xiaoxi Huang
  • Huaxin Huang
  • Beishui Liao
  • Cihua Xu
Article

Abstract

Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. Many works have been done in pragmatics and cognitive linguistics, especially the discussions on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In this paper, a theoretical framework for metaphor understanding based on the embodied mechanism of concept inquiry is proposed. Based on this framework, ontology is introduced as the knowledge representation method in metaphor understanding, and metaphor mapping is formulated as ontology mapping. In line with the conceptual blending theory, a revised conceptual blending framework is presented by adding a lexical ontology and context as the fifth mental space, and a metaphor mapping algorithm is proposed.

Keywords

Metaphor Ontology Conceptual blending Cognitive science Embodied cognition 

References

  1. Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149(1), 91–130.CrossRefGoogle Scholar
  2. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. (2003). The description logic handbook. Cambridge: Cambridge University Press.MATHGoogle Scholar
  3. Boden, M. A. (2003). The creative mind: Myths and mechanisms. London: Routledge.Google Scholar
  4. Chen, K. J., Shu-Ling, H., Yueh-Yin, S., & Yi-Jun, C. (2005). Extended-hownet: A representational framework for concepts. In OntoLex 2005—ontologies and lexical resources IJCNLP-05 workshop, Jeju Island, South Korea.Google Scholar
  5. Dong, Z. D., & Dong, Q. (2006). Hownet and the computation of meaning. Singapore: World Scientific Publishing Company.CrossRefGoogle Scholar
  6. Fauconnier, G., & Turner, M. (1998). Conceptual integration networks. Cognitive Science, 22(2), 133–187.CrossRefGoogle Scholar
  7. Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind’s hidden complexities. London: Basic Books.Google Scholar
  8. Fellbaum, C. (1998). WordNet: An electronic lexical database. Cambridge: MIT Press.MATHGoogle Scholar
  9. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.CrossRefGoogle Scholar
  10. Gomez-Perez, A., & Corcho, O. (2002). Ontology languages for the semantic web. IEEE Intelligent Systems, 17(4), 54–60.CrossRefGoogle Scholar
  11. Grady, J., Oakley, T., & Coulson, S. (1997). Blending and metaphor. In W. G. Raymond, & J. S. Gerard (Eds.), Metaphor in cognitive linguistics (pp. 101–124). Amsterdam: John Benjamins Publishing Company.Google Scholar
  12. Gruber, T. (1993). A translation approach to portable ontology specification. Knowledge Acquisition, 5, 199–220.CrossRefGoogle Scholar
  13. Guarino, N. (1998). Formal ontology and information systems. In Proceedings of 1st International conference on formal ontology in information systems (FOIS’98) (pp. 3–5). Trento, Italy: The IOS Press.Google Scholar
  14. Huang, X. X. (2009). Research on some key issues of metaphor computation. PhD thesis, Zhejiang University, Hangzhou, China (in Chinese).Google Scholar
  15. Huang, X. X., & Zhou, C. L. (2005). A logical approach for metaphor understanding. In Proceedings of 2005 IEEE International conference on natural language processing and knowledge engineering (pp. 268–271). Wuhan, China: IEEE Computer Society.Google Scholar
  16. Huang, X. X., & Zhou C. L. (2007). An owl-based wordnet lexical ontology. Journal of Zhejiang University (Science A), 8(6), 864–870.MathSciNetMATHCrossRefGoogle Scholar
  17. Huang, X. X., Huang, H. X., Xu, C. H., Chen, W., & Wang, R. B. (2011). A novel a novel pattern matching method for chinese metaphor identification and classification. In Proceedings of the 2011 International conference on artificial intelligence and computational intelligence (AICI’11) (pp. 104–114). Taiyuan, China: Springer.Google Scholar
  18. Johnson, M. (1989). The body in the mind: The bodily basis of meaning, imagination, and reason. Chicago: The University of Chicago Press.Google Scholar
  19. Kittay, E. F. (1987). Metaphor, its cognitive force and linguistic structure. Oxford, USA: Oxford University Press.Google Scholar
  20. Kovecses, Z. (2010). Metaphor: A practical introduction, 2nd ed. Oxford: Oxford University Press.Google Scholar
  21. Lakoff, G. (1987). Women, fire, and dangerous things. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  22. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: The University of Chicago Press.Google Scholar
  23. Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought. New York: Basic books.Google Scholar
  24. Lakoff, G., & Turner, M. (1989). More than cool reason: A field guide to poetic metaphor. Chicago: Chicago University Press.CrossRefGoogle Scholar
  25. Maedche, A., & Volz, R. (2001). The ontology extraction and maintenance framework text-to-onto. In Proceedings of the ICDM’ 01 workshop on integrating data mining and knowledge management, California, USA (pp. 1–12).Google Scholar
  26. Mason, Z. J. (2004). Cormet: A computational, corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44.CrossRefGoogle Scholar
  27. Miller, G. A. (1993). Images and models, similes and metaphors. In A. Ortony (Ed.), Metaphor and thought, metaphor and thought (pp. 357–400). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  28. Ogden, C. K., & Richards, I. A. (1989). The meaning of meaning. New York: Mariner Books.Google Scholar
  29. Pereira, F. C. (2007). Creativity and artificial intelligence: A conceptual blending approach. Berlin: Mouton de Gruyter.Google Scholar
  30. Shutova, E. (2010). Models of metaphor in nlp. In Proceedings of the 48th annual meeting of the association for computational linguistics, Uppsala, Sweden (pp. 688–697).Google Scholar
  31. Sowa, J. F. (2000). Knowledge representation: Logical, philosophical, and computational foundations. Pacific Grove, CA: Brooks Cole Publishing Co.Google Scholar
  32. Steinhart, E. C. (2001). The logic of metaphor: Analogous parts of possible worlds. Dordrecht: Kluwer.Google Scholar
  33. Veale, T. (1995). Metaphor, memory and meaning: Symbolic and connectionist issues in metaphor interpretation. PhD thesis, Trinity College, Dublin.Google Scholar
  34. Yang, Y., Zhou, C. L., Ding, X. J., Chen, J. W., & Shi, X. D. (2009). Metaphor recognition: Chmeta, a pattern-based system. Computational Intelligence, 25(4), 265–301. doi:10.1111/j.1467-8640.2009.00349.x.MathSciNetCrossRefGoogle Scholar
  35. Zhou, C. L. (2003). Introduction to mind computation. Beijing: Tsinghua University Press.Google Scholar
  36. Zhou, C. L., Yang, Y., & Huang, X. X. (2007). Computational mechanisms for metaphor in languages: A survey. Journal Computer Science and Technology, 22(2), 308–319.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Xiaoxi Huang
    • 2
  • Huaxin Huang
    • 1
  • Beishui Liao
    • 1
  • Cihua Xu
    • 1
  1. 1.Center for the Study of Language and CognitionZhejiang UniversityHangzhouChina
  2. 2.The Institute of Computer Application TechnologyHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations