Abstract
One requirement for language systems for knowledge based applications is to handle large knowledge bases efficiently. Large knowledge bases written in Prolog have a large load time from secondary storage. We describe how a Prolog system can be supported with a clustering concept for minimizing both the load time and the loaded code of large Prolog knowledge bases, which additionally enables an efficient cluster buffer managment, if knowledge base size exceeds the available main memory. Clusters are knowledge base partitions of equal size which are generated at compile time and contain semantically related clauses. The paper focusses on a comparative performance evaluation of a Prolog system supported with various cluster replacement strategies and compares “intelligent” cluster replacement strategies with conventional replacement strategies known from operating systems. The result of the performance evaluation is that the load time for Prolog knowledge bases can be reduced by using “intelligent” instead of conventional cluster replacement strategies.
Published Version
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