A data structure for subsumption-based tabling in top-down resolution engines for data-intensive logic applications
Using tables in top-down resolution engines is an effective way of optimization by avoiding recomputation of formerly derived facts. This effect can be even more exploited if, instead of considering only identical subgoals, facts from subsuming sugoals are reused. This is particularly of interest if recalculation is expensive, e.g. due to data transmission cost or the mere volume of data to be processed.
We present a data structure for subsumption-based tabling in subgoal-oriented resolution. Our data structure is based on hB-Trees, a multi-attribute indexing structure which emerged from research on database management systems. Hence, our data structure inherits desirable properties such as its use as efficient indexing structure for secondary storage.
KeywordsLogic for Artificial Intelligence Intelligent Information Retrieval Subsumption-based Tabling Information Integration
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