Designing for scalability in a knowledge fusion system

The knowledge reuse and fusion/transformation (KRAFT) project has defined a generic agent-based architecture to support knowledge fusion - 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 appl ied in problem-solving. KRAFT focuses on knowledge in the form of constrain ts expressed against an object data model defined by a shared ontology. KRA FT employs three kinds of agent: facilitators locate appropriate on-line so urces of knowledge; wrappers transform heterogeneous knowledge to a homogen eous constraint interchange format; mediators fuse the constraints together with associated data to form a dynamically-composed constraint satisfactio n problem, which is then passed to an existing constraint solver engine to compute solutions. The KRAFT architecture has been designed to be scalable to large numbers of agents; this paper describes the features of the architecture designed to support scalability. In particular, we examine static techniques that under pin the growth of large-scale KRAFT networks, and dynamic techniques that a llow reorganisation of a KRAFT network as it increases in scale. (C) 2001 E lsevier Science B.V. All rights reserved.