Query Rewriting for Existential Rules with Compiled Preorder
Authors: Melanie Konig, Michel Leclere, Marie-Laure Mugnier
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm was implemented in Java, as an extension of the query rewriting prototype PURE. All tests were performed on a DELL machine with a processor at 3.60 GHz and 16 GB of RAM. As benchmarks dedicated to existential rules are not available yet, and in order to compare with other tools producing UCQs, which are mostly restricted to DL-Lite, we considered rule bases obtained by translation of DL-Lite R ontologies: first, the widely used benchmark introduced in [Pérez-Urbina et al., 2009] (i.e., ADOLENA (A), STOCKEXCHANGE (S), UNIVERSITY (U) and VICODI (V)); second, very large ontologies built from Open Galen2 (G) and OBOProtein (O), and used in [Trivela et al., 2013], which respectively contain more than 53k and 34k rules, with 54% and 64% of compilable rules. Each ontology is provided with 5 handcrafted queries. Timeout was set to 10 minutes. Due to space limitation, we list only parts of the experiments. We first evaluated the impact of rule compilation on the rewriting process, w.r.t. rewriting sizes and runtime respectively. |
| Researcher Affiliation | Academia | M elanie K onig, Michel Lecl ere, Marie-Laure Mugnier University of Montpellier, Inria, CNRS Montpellier, France |
| Pseudocode | Yes | Algorithm 1: c-REWRITING ALGORITHM |
| Open Source Code | Yes | Available at https://github.com/graphik-team/graal |
| Open Datasets | Yes | first, the widely used benchmark introduced in [Pérez-Urbina et al., 2009] (i.e., ADOLENA (A), STOCKEXCHANGE (S), UNIVERSITY (U) and VICODI (V)); second, very large ontologies built from Open Galen2 (G) and OBOProtein (O), and used in [Trivela et al., 2013] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets relevant to its experimental setup, which involves query rewriting on ontologies, not traditional machine learning model training. |
| Hardware Specification | Yes | All tests were performed on a DELL machine with a processor at 3.60 GHz and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions 'implemented in Java' and uses 'query rewriting prototype PURE' but does not provide specific version numbers for Java or PURE, which are needed for reproducible software dependencies. |
| Experiment Setup | Yes | Timeout was set to 10 minutes. |