Query-Subquery Nets

TL;DR

This paper introduces a goal-directed framework called QSQN for efficient query evaluation on Horn knowledge bases, emphasizing reduced redundancy, flexibility, and controlled storage access, with proven soundness and completeness.

Contribution

It presents the first generic evaluation framework for Horn knowledge base queries that is goal-directed, set-oriented, and adaptable with multiple control strategies.

Findings

Proves soundness and completeness of the QSQN framework.

Shows fixed-term depth bounds lead to PTIME data complexity.

Demonstrates incorporation of tail recursion elimination into the framework.

Abstract

We formulate query-subquery nets and use them to create the first framework for developing algorithms for evaluating queries to Horn knowledge bases with the properties that: the approach is goal-directed; each subquery is processed only once and each supplement tuple, if desired, is transferred only once; operations are done set-at-a-time; and any control strategy can be used. Our intention is to increase efficiency of query processing by eliminating redundant computation, increasing flexibility and reducing the number of accesses to the secondary storage. The framework forms a generic evaluation method called QSQN. To deal with function symbols, we use a term-depth bound for atoms and substitutions occurring in the computation and propose to use iterative deepening search which iteratively increases the term-depth bound. We prove soundness and completeness of our generic evaluation method and show that, when the term-depth bound is fixed, the method has PTIME data complexity. We also present how tail recursion elimination can be incorporated into our framework and propose two exemplary control strategies, one is to reduce the number of accesses to the secondary storage, while the other is depth-first search.