Combining Existential Rules and Description Logics
Authors: Antoine Amarilli, Michael Benedikt
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work investigates how to get the best of both worlds: having decidable existential rules on arbitrary arity relations, while allowing rich description logics, including functionality constraints, on arity-two relations. We first show negative results on combining such decidable languages. Second, we introduce an expressive set of existential rules (frontier-one rules with a certain restriction) which can be combined with powerful constraints on arity-two relations (e.g. GC2,ALCQIb) while retaining decidable query answering. Further, we provide conditions to add functionality constraints on the higher-arity relations. |
| Researcher Affiliation | Academia | Antoine Amarilli Michael Benedikt Télécom Paris Tech; Institut Mines Télécom; CNRS LTCI University of Oxford antoine.amarilli@telecom-paristech.fr michael.benedikt@cs.ox.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on decidability and logical constraints, not empirical evaluation on datasets. Therefore, it does not provide access information for a dataset. |
| Dataset Splits | No | This is a theoretical paper focused on decidability and logical constraints, not empirical evaluation on datasets. Therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | This is a theoretical paper. It does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper. It does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper. It does not include specific experimental setup details such as hyperparameters or training configurations. |