Description logic knowledge bases traditionally contain a set of axioms (Tbox) describing background knowledge. DL reasoners generally handle axioms by using the lazy unfolding technique, which reduces the nondeterminism introduced by axioms. Axiom absorption is an optimization technique that rewrites axioms into the unfoldable part of the Tbox suitable for lazy unfolding.;Absorptions are generally employed in DL reasoners in a mostly uniform way regardless of the characteristics of an input knowledge base. Though there exist a number of absorptions, their overall effectiveness remains to be improved, especially when a large quantity of complex axioms are present in the knowledge bases, which is well beyond the capability of any single absorption technique.;To ameliorate absorption techniques, this thesis presents a framework applying AI planning to axiom absorption. In this framework, a state space planner is used to encode state-of-the-art absorption techniques. Some designed heuristics concerning the characteristics of an input KB are utilized for the cost estimation during planning. The planner first applies appropriate absorptions to axioms, then it produces a solution with a minimized cost. Such a solution automatically organizes absorptions in a certain sequence to maximize the number of axioms for absorptions. Compared to a predetermined or fixed order of applying absorption techniques, the proposed framework benefits from the advantages to consider more absorption alternatives, which tends to be more flexible and effective.
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机译:转换术语± Sup> [n i Sub>] f(+/-) min sup>的条件最小化结构的逻辑动态过程的方法Sub> AND ± Sup> [m i Sub>] f(+/-) min Sub>在功能添加结构中± Sup> f < Sub> 1 Sub>(Σ RU Sub>) min Sub>,不带纹波f 1 Sub>(± Sup>←←)和循环ΔtΣ Sub>→5∙f(&)-和5个条件逻辑函数f(&)-,并通过三元数系统的算术公理同时转换术语参数的过程f RU Sub>(+ 1,0,-1)及其实现其的功能结构(俄罗斯逻辑版本)