Expressivity of Datalog Variants — Completing the Picture
Authors: Sebastian Rudolph, Michaël Thomazo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 sebastian.rudolph@tu-dresden.de Micha el Thomazo Inria, France michael.thomazo@inria.fr |
| 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. |