A knowledge base (KB) is a collection of factual information pertaining to the objects of specialized domains or application areas. KB information may be acquired and represented using language paradigms which are based on formalisms of predicate calculus. Usually, the domains are not necessarily distinct due to the interrelatedness of the components of the problem or interdependency of the objects. Therefore, this interdependency could generate long search paths or references to the KB objects, particularly for large KB data. 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 into clusters and localizes the interdependency within the clusters. Therefore, the clusters offer opportunities for mapping the data into distributed or parallel processing environments to facilitate computational efficiency. This article focuses on methods for structuring, partitioning, and clustering logic-based KB data (rules and facts) for distributed computations.