Abstract
Knowledge-base data are constructed from domains and subdomains of specific problem or application areas. Usually, the domains are not necessarily distinct because the original problem may have many interrelated components. As a result, the processing of the data becomes lengthy and windable. However, KB data can be reorganized into groups or clusters using some common relational information of the data objects. The reorganization process isolates the data and localizes the interdependency within the clusters, leaving weak linkages between clusters. Therefore, the clusters offer opportunities for mapping the data into distributed or parallel processing environments to facilitate a computational efficiency. The focus of this paper is on methods for structuring, partitioning, and clustering KB data for distributed computations.
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