Encoding Conceptual Graphs by Labeling RAAM

Abstract

The meaning of medical texts is not automatically recognized by computers. A representation of this information is strongly recommanded to allow medical texts databases queries. The conceptual graph formalism developed by Sowa [Sow84] is a knowledge representation language initially designed to capture the meaning of natural language. Conceptual graphs have been used in many natural language understanding works [BRS92, VZB+93, Ber91]. In this paper we discuss the possibility to memorize and retrieve natural language sentences and especially medical language sentences given in this kind of formalism with the use of the LRAAM model [Spe93b, Spe93a]. In Section 2 we explain the idea underlying conceptual graphs. In Section 3 we briefly expose the access by content capabilities of the LRAAM and suggest a generalization of the access by content procedures introducing the concept of Generalized Hopfield Network. A discussion on the impact of this generalization on knowledge extraction from a database of conceptual graphs is given in the conclusion.