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..
Characterization of the Expressivity of Existential Rule Queries
Authors: Sebastian Rudolph, Michaël Thomazo
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that, conversely, every homomorphism-closed recursively enumerable query can be expressed as an existential rule query, thus arriving at a precise characterization of existential rules by model-theoretic and computational properties. Although the result is very intuitive, the proof turns out to be non-trivial. |
| Researcher Affiliation | Academia | Technische Universit at Dresden, Germany EMAIL |
| Pseudocode | No | The paper provides formal rules (e.g., equations 1-10) but does not include structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper provides a link to a technical report for additional proofs ("https://ddll.inf.tudresden.de/web/Techreport3019/en") but does not state that source code for the described methodology is available. |
| Open Datasets | No | This paper is theoretical and does not use or reference any empirical datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not describe any training/validation/test dataset splits as it does not involve empirical experiments. |
| Hardware Specification | No | This paper is theoretical and does not discuss hardware specifications used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |