Efficient Query Answering over Expressive Inconsistent Description Logics
Authors: Eleni Tsalapati, Giorgos Stoilos, Giorgos Stamou, George Koletsos
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented our techniques and conducted an experimental evaluation obtaining encouraging results as both our IARand ICAR-answering approaches are far more efficient than existing available IAR-based answering systems. |
| Researcher Affiliation | Academia | Eleni Tsalapati,1 Giorgos Stoilos,2 Giorgos Stamou,1 and George Koletsos1 1School of Electrical and Computer Engineering, 2Department of Informatics, National Technical University of Athens, Greece Athens University of Economics and Business |
| Pseudocode | Yes | Algorithm 1 Approx Ans(T , A) and Algorithm 2 ABox IARRepair(T , A) |
| Open Source Code | Yes | We implemented our ICAR(both the standard and the optimised one) and IAR-answering approaches into the prototype system Sa QAI2 (Saturation based Query Answering under Inconsistencies); in the following the various versions of Sa QAI (standard/optimised ICAR and IAR) are called Sa Qic, Sa Qic op, and Sa Qia, respectively. 2http://image.ece.ntua.gr/ etsalap/Sa QAI |
| Open Datasets | Yes | For the evaluation we used the experimental setting proposed in [Bienvenu et al., 2014] which consists of a DL-Lite version of the LUBM9 20 ontology [Lutz et al., 2013] extended with additional negative inclusions, a set of test queries, and several inconsistent ABoxes. |
| Dataset Splits | No | For the evaluation we used the experimental setting proposed in [Bienvenu et al., 2014] which consists of a DL-Lite version of the LUBM9 20 ontology [Lutz et al., 2013] extended with additional negative inclusions, a set of test queries, and several inconsistent ABoxes. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided for the experimental setup. |
| Software Dependencies | No | Our system uses Graph DB [Kiryakov et al., 2010] as an ABox-saturation system, Hydrowl [Stoilos, 2014] to compute completions, and Rapid [Trivela et al., 2015] for rewriting. |
| Experiment Setup | No | For the evaluation we used the experimental setting proposed in [Bienvenu et al., 2014] which consists of a DL-Lite version of the LUBM9 20 ontology [Lutz et al., 2013] extended with additional negative inclusions, a set of test queries, and several inconsistent ABoxes. |