Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Combining Existential Rules and Description Logics
Authors: Antoine Amarilli, Michael Benedikt
IJCAI 2015 | Venue PDF | 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 EMAIL EMAIL |
| 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. |