Conceptual Graphs for Knowledge Representation

Artificial Intelligence and cognitive science are the two fields devoted to the study and development of knowledge-based systems (KBS). Over the past 25years, researchers have proposed several approaches for modeling knowledge in KBS, including several kinds of formalism such as semantic networks, frames, and logics. In the early 1980s, J.F. Sowa introduced the conceptual graph (CG) theory which provides a knowledge representation framework consisting of a form of logic with a graph notationand integrating several features from semantic net and frame representations. Since that time, several research teams over the world have been working on the application and extension of CG theory in various domains ranging from natural language processing to database modeling and machine learning. This volume contains selected papers fromthe international conference on Conceptual Structures held in the city of Quebec, Canada, August 4-7, 1993. The volume opens with invited papers by J.F. Sowa, B.R. Gaines, and J. Barwise.

Inhalt
Relating diagrams to logic.- Representation, discourse, logic and truth: Situating knowledge technology.- Heterogeneous reasoning.- A comparison between conceptual graphs and KL-ONE.- The term definition operators of ontolingua and of the conceptual graph formalism: a comparison.- Implementing conceptual graphs in a RDBMS.- A conceptual graphs approach to conceptual schema integration.- Conceptual graphs for relational Databases.- Inducing a CG representation for basic-level categorization of verbs.- The representation of linguistic information in an approach used for modelling temporal knowledge in discourses.- Representing natural language causality in Conceptual Graphs: The Higher order conceptual relation problem.- Executable conceptual structures.- Inverting resolution with conceptual graphs.- Towards domain-independent machine intelligence.- Efficient retrieval from hierarchies of objects using lattice operations.- Characterization and algorithmic recognition of canonical conceptual graphs.- A conceptual semantics ontology for conceptual graphs.- Acquiring temporal knowledge from schedules.- Conceptual structures for modeling in CIM.- Elicitation of taxonomies based on the use of conceptual graph operators.- Presenting a peirce logic based inference engine and theorem prover for conceptual graphs.- Operations on conceptual structures and Peirce's system of existential graphs.- Modal logics for conceptual graphs.- Fuzzy conceptual graphs.