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Vol: 53(67) No: 2 / June 2008 Knowledge Base Representation in a Grammar Induction System with Extended Conceptual Graph Erika Baksa-Varga Department of Information Technology, University of Miskolc, Faculty of Mechanical Engineering and Informatics, H-3515 Miskolc-Egyetemváros, Hungary, phone: (36 46) 565-136, e-mail: vargae@iit.uni-miskolc.hu László Kovács Department of Information Technology, University of Miskolc, Faculty of Mechanical Engineering and Informatics, H-3515 Miskolc-Egyetemváros, Hungary, e-mail: kovacs@iit.uni-miskolc.hu Keywords: knowledge representation, semantic models Abstract The goal of our research is to develop a grammar induction system that works on the basis of statistical learning methods. Our approach is based on a model where free text is annotated with a semantic ontology description. In the model, the main task is to learn the association rules between free text sentences and ontology representations. The novelties of the project are the use of ontology as support for grammar induction, and the sentence-based approach. The paper starts with Peirce’s theory of signs and its consequent ideas as the background for the definition of signal processing in intelligent artificial agents, which build up their internal knowledge base from the received signals. The knowledge base is represented by an ontology. As a first step, an appropriate formalism is searched for the semantic model that represents the knowledge base of agents to be converted into sentences. For this purpose we have created a comparative review of semantic models. According to our analyses, traditional semantic modeling languages are not adequate for reflecting the process of conceptualization. Therefore we developed, and in this paper we introduce Extended Conceptual Graph, a novel semantic model for representing the knowledge base in a grammar induction system. 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