Orar: Ontology Reasoning via Abstraction Refinement
Orar is a scalable ontology reasoner. Its central technique allows for reducing reasoning of an ontology with a large dataset/ABox to reasoning of a smaller (compressed) abstraction of this ontology. Currently Orar supports the Description Logic Horn SHOIF.
Orar is being developed by Birte Glimm, Yevgeny Kazakov, and Trung Kien Tran with the support from the DFG funded project for processing large amounts of data in ontologies via Abstraction and Refinement.
- Orar is available here
- Source code is available here
- Instructions on how to run Orar are available here
- Birte Glimm, Yevgeny Kazakov, and Trung-Kien Tran. Ontology Materialization by Abstraction Refinement in Horn SHOIF. AAAI 17 (to appear). Download the technical report.
- Birte Glimm, Yevgeny Kazakov, and Trung-Kien Tran. Scalable Reasoning by Abstraction Beyond DL-Lite. RR 2016. Download a preprint.
- Birte Glimm, Yevgeny Kazakov, Thorsten Liebig, Trung-Kien Tran, Vincent Vialard. Abstraction Refinement for Ontology Materialization. ISWC 2014. Download the technical report.
phone: +49 (0)731/50-24166
Trung Kien Tran
University of Ulm
Institute of Artificial Intelligence