Theoretical basis for hierarchical incremental knowledge acquisition
International Journal of Human-Computer Studies
The Knowledge Level Reinterpreted: Modeling How Systems Interact
Machine Learning
Acquisition of Search Knowledge
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Holism and Incremental Knowledge Acquisition
EKAW '99 Proceedings of the 11th European Workshop on Knowledge Acquisition, Modeling and Management
Supporting Inheritance Mechanisms in Ontology Representation
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
MonitoringKnowledge Acquisition Instead of Evaluating Knowledge Bases
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Informed Selection of Training Examples for Knowledge Refinement
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
PEBM: A Probabilistic Exemplar Based Model
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Using the F2 OODBMS to Support Incremental Knowledge Acquisition
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Evolution Features of the F2 OODBMS
Proceedings of the 4th International Conference on Database Systems for Advanced Applications (DASFAA)
Knowledge Acquisition First, Modelling Later
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
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Using a database management system (DBS) to build a knowledge base system (KBS) is sometimes desirable because DBS systems allow management of large sets of rules, control of concurrent access and managing multiple knowledge bases simultaneously. In this paper, we describe how to build a KBS using a database management system DBMS for its schema evolution ability. This allows the use of an Object Oriented DMBS (OODBMS) to manage the consistency of an incrementally built hierarchical knowledge base (KB). The underlying knowledge representation scheme, which we use, is our Nested Ripple Down Rules (NRDR).An NRDR KB evolves into a hierarchy of concepts where each concept is defined as a collection of hierarchical rules with exceptions. To modify a concept definition, exception rules are added by a domain expert, they are never deleted or modified. This eases maintenance and development of a concept definition, but may cause inconsistencies to occur in the KB. We analyse the relation between cases and rules as the knowledge base evolves and as these inconsistencies occur. We explore what specific features an OO database model should accommodate to be used to implement an NRDR KB. The aim is that this in turn allows the use of the built-in mechanisms to manage the consistency of an evolving NRDR conceptual hierarchy. The significance of this paper is two folds: first, it describes an efficient mechanism maintaining consistency of an evolving classification hierarchy, using built-in schema evolution features of an OODBMS. Second, it proposes an intelligent interface for an OODBMS, to allow intelligent classification queries over stored objects.