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.
On Querying Incomplete Information in Databases under Bag Semantics
Authors: Marco Console, Paolo Guagliardo, Leonid Libkin
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,615 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,366 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 | We show that the behavior of positive queries is different under bag semantics: finding the minimum number of occurrences can still be done efficiently, but for maximum it becomes intractable. We use these results to investigate approximation schemes for computing certain answers to arbitrary first-order queries that have been proposed for set semantics. One of them cannot be adapted to bags, as it relies on the intractable maxima of occurrences, but another scheme only deals with minima, and we show how to adapt it to bag semantics without losing efficiency. |
| Researcher Affiliation | Academia | Marco Console , Paolo Guagliardo and Leonid Libkin University of Edinburgh |
| Pseudocode | No | The paper provides formal definitions and theoretical proofs, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve validation sets for empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used. |
| Software Dependencies | No | The paper is theoretical and does not specify 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 configurations. |