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 [1].
Combining Existential Rules and Transitivity: Next Steps
Authors: Jean-François Baget, Meghyn Bienvenu, Marie-Laure Mugnier, Swan Rocher
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely a GRD (acyclic graph of rule dependencies), which clarifies the landscape of finite expansion sets of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment. |
| Researcher Affiliation | Academia | Jean-Franc ois Baget Inria, CNRS, Univ. Montpellier Montpellier, France Meghyn Bienvenu CNRS, Univ. Paris-Sud Orsay, France Marie-Laure Mugnier Univ. Montpellier, Inria, CNRS Montpellier, France Swan Rocher Univ. Montpellier, Inria, CNRS Montpellier, France |
| Pseudocode | No | The paper describes the steps of its query rewriting algorithm in prose, for example in Section 4.2 'Overview of the Algorithm', but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments or specify any hardware used for running them. |
| Software Dependencies | No | The paper mentions abstract concepts like 'Datalog engine' but does not provide specific software names with version numbers that would be needed to replicate any experimental setup. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |