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.
Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Cardinality-Based Repairs
Authors: Thomas Lukasiewicz, Enrico Malizia, Andrius Vaicenavičius2962-2969
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,630 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,545 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 | This paper continues this line of research on cardinality-maximal consistent query answering, and we analyze the complexity of the above three inconsistency-tolerant query answering semantics for a wide range of Datalog languages and for several different complexity measures: |
| Researcher Affiliation | Academia | Thomas Lukasiewicz Department of Computer Science University of Oxford, UK Enrico Malizia Department of Computer Science University of Exeter, UK Andrius Vaicenaviˇcius Department of Computer Science University of Oxford, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe training on datasets for empirical experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, so no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for empirical experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for empirical experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide details on an experimental setup, such as hyperparameters or training settings. |