Many language-oriented problems cannot be solved without semantic memory containing descriptions of concepts at different level of details. Automatic creation of semantic memories is a great challenge even for the simplest knowledge representation methods based on relations between concepts and keywords. Semantic memory based on such simple knowledge representation facilitates implementation of quite interesting linguistic competences that have not yet been demonstrated by more sophisticated rule or frame-based knowledge bases, for example CYC. These linguistic abilities include word games, such as the twenty questions game, that may be implemented using semantic memory built on relational model for knowledge representation. Creation of large-scale knowledge base for semantic memory involves mining structured information sources (ontologies, dictionaries, encyclopedic entries) and free texts (textbooks and internet sources). Quality of this knowledge may be improved using collaborative projects in which systems that already possess some linguistic competence actively interact with human users, mining their knowledge. In this article three dialog scenarios for mining human knowledge are introduced, and the data acquired into semantic memory structures through such interaction is described.

Reference: Proc. of the 20th Int. Joint Conference on Neural Networks (IJCNN), Orlando, IEEE Press, August 2007, pp. 1156

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