Indexes, glossaries and thesauri are all ways of mapping the knowledge structures that exist implicitly in books and other sources of information. In the field of AI (Artificial Intelligence) there also exists the need to be able to represent knowledge (and meaning), in order to support communication between people and machines. One widely used knowledge representation formalism is that of conceptual graphs, whose building blocks are concepts and conceptual relations. In the following conceptual graph for the phrase “man biting dog” ([Sowa 1984]), square brackets denote concepts ('man', 'bite', 'dog'), and parentheses denote relations ('agent', 'object'): [man] <- (agent) <- [bite] -> (object) -> [dog] Similar graph structures have been implemented in various forms under names such as “semantic nets”, “associative nets”, “partioned nets” and “knowledge” (or “conceptual”) “maps” in many AI systems. The earliest forms, called existential graphs, were invented by the philosopher Charles Sanders Peirce at the end of the 19th century as a graphical notation for symbolic logic. One of the most completely worked out schemes, the conceptual graphs developed by John Sowa and his collaborators ([Sowa 2000]), is claimed to be completely isomorphic with first order logic.