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  Research :: Knowledge Representation

Humans and machines represent their knowledge in many ways. Efficient communication of knowledge, whether between humans or between humans and machines, relies on an understanding of knowledge representation. Formal knowledge encoding schemes and precise semantic theories for existing notations are needed in order to help machines use human knowledge adequately.

Using techniques from logic, AI, and cognitive sciences, we analyze the semantics of notations, such as mathematical diagrams and maps, and the use of formal notations to encode intuitive meanings, particularly in a social context involving human and machine agents. Other work in this area includes formalizing parts of everyday knowledge in forms suitable for machine inference, and the design of languages for semantic markup of web pages.

An improved understanding of knowledge representation is fundamental to much work in AI and cognitive science. Communication between machines, such as software agents and the Semantic Web, will also benefit from this research. In addition, better knowledge representation will enhance education, the communication of knowledge between humans.