High Performance Absorption Algorithms for Terminological Reasoning

When reasoning with description logic (DL) knowledge bases (KBs), performance is of critical concern in real applications, especially when these KBs contain a large number of axioms. To improve the performance, axiom absorption has been proven to be one of the most effective optimization techniques. The well-known algorithms for axiom absorption, however, still heavily depend on the order and the format of the axioms occurring in KBs. In addition, in many cases, there exist some restrictions in these algorithms which prevent axioms from being absorbed. The design of absorption algorithms for optimal reasoning is still an open problem. In this paper, we propose some new algorithms to absorb axioms in a KB to improve the reasoning performance. The experimental tests we conducted are mostly based on synthetic benchmarks derived from common cases found in real KBs. The experimental evaluation demonstrates a significant runtime improvement.