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.