Dean M. Jones

The KRAFT architecture for knowledge fusion and transformation

This paper describes the Knowledge Reuse And Fusion/Transformation (KRAFT) architecture which supports the fusion of knowledge from multiple, distributed, heterogeneous sources. The architecture uses constraints as a common knowledge interchange format, expressed against a common ontology. Knowledge held in local sources can be transformed into a common constraint language, and fused with knowledge from other sources. The fused knowledge is then used to solve some problem or deliver some information to a user. Problem solving in KRAFT typically exploits pre-existing constraint solvers. KRAFT uses an open and flexible agent architecture in which knowledge sources, knowledge fusing entities and users are all represented by independent KRAFT agents, communicating using a messaging protocol. Facilitator agents perform matchmaking and brokerage services between the various kinds of agent. KRAFT is being applied to an example application in the domain of network data services design.

KRAFT: knowledge fusion from distributed databases and knowledge bases

The KRAFT project aims to investigate how a distributed architecture can support the transformation and reuse of a particular class of knowledge, namely constraints, and to fuse this knowledge so as to gain added value, by using it for constraint solving or data retrieval.

KRAFT: AN AGENT ARCHITECTURE FOR KNOWLEDGE FUSION

Knowledge fusion refers to the process of locating and extracting knowledge from multiple, heterogeneous on-line sources, and transforming it so that the union of the knowledge can be applied in problem-solving. The KRAFT project has defined a generic agent-based architecture to support fusion of knowledge in the form of constraints expressed against an object data model. KRAFT employs three kinds of agent: facilitators locate appropriate on-line sources of knowledge; wrappers transform heterogeneous knowledge to a homogeneous constraint interchange format; mediators fuse the constraints together with associated data to form a dynamically-composed constraint satisfaction problem, which is then passed to an existing constraint solver engine to compute solutions. The paper presents the KRAFT architecture and the three kinds of agent, and includes a description of a demonstration KRAFT application in the domain of telecommunications service provision.

Toward principles for the representation of hierarchical knowledge in formal ontologies

Abstract Early ontological engineering methodologies have necessarily focussed on the management of the whole ontology development process. There is a corresponding need to provide advice to the ontological engineer on the finer details of ontology construction. Here, we specifically address the representation of hierarchical relationships in an ontology. We identify five types of problem that may be encountered in moving from an informal description of a domain to a formal representation of hierarchical knowledge. Each problem type is discussed from the perspective of knowledge sharing and examples from biological ontologies are used to illustrate each type.

Acquisition of Conceptual Structure in Scientific Theories

This paper details an approach to the acquisition of a specific kind of knowledge that found in causal scientific theories. We are especially concerned with the conceptual structure found in such theories as we assume them to be cognitive objects. The acquisition of this conceptual structure should take into account the structure of the underlying cognitive models. We have developed a software tool that assists in the early acquisition stages of the knowledge-based system (KBS) development cycle.

PRONTO - Ontology-based Evaluation of Knowledge Based Systems

In this paper we examine some of the ways in which an ontology can be used to assist in the evaluation of knowledge-based systems. Key elements of the support provided by the ontology relate to attempting to give coherence to the domain conceptualisation; making the role of experts in evaluation more structured and less at the mercy of interpretation; constraining the number of test cases required to give good coverage of the possible cases; and structuring the testing to give better assurance of its efficacy, and provide for a possible basis for greater automation of the testing process. The discussion is focussed on the development of a prototype software tool to support the approach and this is illustrated using a simple, well known, example relating to the identification of animals.