Model-theoretic Characterizations of Existential Rule Languages
Authors: Heng Zhang, Yan Zhang, Guifei Jiang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for the class of arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these results, complexity bounds for the rewritability of above languages are also identified. |
| Researcher Affiliation | Academia | Heng Zhang 1 , Yan Zhang 2,4 and Guifei Jiang 3 1College of Intelligence and Computing, Tianjin University, China 2School of Computer, Data and Mathematical Sciences, Western Sydney University, Australia 3College of Software, Nankai University, China 4School of Computer Science & Technology, Huazhong University of Science & Technology, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper provides a link to an arXiv version of the paper for proof details ('https://arxiv.org/abs/2001.08688'), but it does not state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets, training data, or public datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |