Dataset and Software for Abstraction Materialization Maintenance

Abstraction Refinement is a technique which allows for reducing materialization of an ontology with a large ABox to materialization of a smaller (compressed) `abstraction' of this ontology. The corresponding conference paper shows how Abstraction Refinement can be adopted for incremental ABox materialization by combining it with the well-known DRed algorithm for materialization maintenance. The combination is non-trivial and to preserve correctness, already Horn ALCHI requires more complex abstractions. Nevertheless, significant benefits can be obtained for synthetic and real-world ontologies. This data set contains the source code for the implementation as well as the used test data and test runners to reproduce the results reported in the paper.

抽象精化（Abstraction Refinement）是一种可将带有大规模ABox的本体的物化操作，归约为该本体的精简（压缩）‘抽象’版本的物化操作的技术。对应的会议论文阐述了如何通过将抽象精化与用于物化维护的经典DRed算法相结合，将其应用于增量ABox物化场景。该结合方法并非平凡的，且为保证结果的正确性，仅针对Horn ALCHI就需要更为复杂的抽象机制。尽管如此，该方法在合成本体与真实世界本体上仍可获得显著收益。本数据集包含了该实现的源代码，以及用于复现论文中所报告实验结果的测试数据与测试运行器。