Paper Info
Title
A technique for customizing object-oriented knowledge representation systems, with an application to network problem management
Abstract
Over the last few years, object-oriented techniques have gained an increasing recognition both in software engineering and in AI. Object-oriented systems present undisputable advantages and provide features that make them really suitable to represent knowledge. However, our practical experience in using these techniques for knowledge representation led us to discover that object-oriented systems also present serious drawbacks, essentially due to their lack of expressive power. These drawbacks can really be felt when modelling domains characterized by a wide variety of knowledge. This paper introduces the notion of Representation Cluster, which allows to provide any object-oriented system with customizable knowledge representation formalisms. These formalisms enable to express and handle diverse kinds of knowledge using only their natural high-level properties. Such an approach speeds up knowledge bases development, makes them clearer, natural, and avoids a great deal of code writing. Its advantages are illustrated on two realistic examples extracted from the knowledge representation system of an expert system shell dedicated to real-time network troubleshooting.
Year
Venue
Keywords
1989
IJCAI
present serious drawback,object-oriented system,knowledge representation,expert system shell,knowledge representation system,knowledge bases development,object-oriented technique,natural high-level property,network problem management,present undisputable advantage,object-oriented knowledge representation system,customizable knowledge representation formalisms,real time,knowledge base,software engineering,expert system,object oriented,expressive power
Field
DocType
Citations 
Procedural knowledge,Knowledge representation and reasoning,Domain knowledge,Computer science,Expert system,Knowledge-based systems,Artificial intelligence,Knowledge extraction,Knowledge base,Machine learning,Open Knowledge Base Connectivity
Conference
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Lisiane Goffaux100.34
Robert Mathonet2124.80