Novel neural network for four-term analogy based on area representation

Kenji Mizoguchi, Masafumi Hagiwara

Research output: Contribution to conferencePaper

Abstract

In this paper, we propose a novel neural network for four-term analogy based on area representation. It can deal with four-term analogy such as `teacher:student = doctor:?'. The proposed network is composed of three map layers and an input layer. The area representation method based on Kohonen Feature Map (KFM) is employed in order to represent knowledge, so that similar concepts are mapped in nearer area in the map layer. The proposed mechanism in the map layer can realize the movement of the excited area to the near area. We carried out some computer simulations and confirmed the followings: (1) similar concepts are mapped in the nearer area in the map layer; (2) the excited area moves among similar concepts; (3) the proposed network realizes four-term analogy; (4) the network is robust for the lack of connections.

Original languageEnglish
Pages1144-1149
Number of pages6
Publication statusPublished - 1999 Dec 1
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Mizoguchi, K., & Hagiwara, M. (1999). Novel neural network for four-term analogy based on area representation. 1144-1149. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .