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.

{"references": ["Markus Brenner and Birte Glimm. Embracing Change by Abstraction Materialization Maintenance for Large ABoxes. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), 2018."]}

Software, data sets and empirical results for the IJCAI 2018 publication "Embracing Change by Abstraction Materialization Maintenance for Large ABoxes"