Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Characterizing the Program Expressive Power of Existential Rule Languages
Authors: Heng Zhang, Guifei Jiang5950-5957
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,326 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,730 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we establish a number of novel characterizations for the program expressive power of several important existential rule languages, including tuple-generating dependencies (TGDs), linear TGDs, as well as disjunctive TGDs. The characterizations employ natural model-theoretic properties, and automata-theoretic properties sometimes, which thus provide powerful tools for identifying the definability of domain knowledge for OMQA in these languages. |
| Researcher Affiliation | Academia | Heng Zhang1, Guifei Jiang2 1 College of Intelligence and Computing, Tianjin University, China 2 College of Software, Nankai University, China |
| Pseudocode | No | The paper describes procedures and transformations using mathematical notation and logical rules, but it does not include pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not contain any statements or links indicating that the authors have released open-source code for the methodology described in the paper. |
| Open Datasets | No | This is a theoretical paper and does not describe experiments involving datasets, training, or public dataset availability. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments or dataset validation splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup or specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup, hyperparameters, or training configurations. |