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.
Expressivity of Datalog Variants — Completing the Picture
Authors: Sebastian Rudolph, Michaël Thomazo
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,248 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,714 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 | Computational and model-theoretic properties of logical languages constitute a central field of research in logic-based knowledge representation. Datalog is a very popular formalism, a de-facto standard for expressing and querying knowledge. Diverse results exist regarding the expressivity of Datalog and its extension by input negation (semipositive Datalog) and/or a linear order (orderinvariant Datalog). |
| Researcher Affiliation | Academia | Sebastian Rudolph TU Dresden, Germany EMAIL Micha el Thomazo Inria, France EMAIL |
| Pseudocode | No | The paper contains formal definitions and Datalog rules (e.g., Figure 6), but not structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention using datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe data splitting for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper discusses logical formalisms like Datalog but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |