Notice: 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. Full accuracy metrics and methodology are described in [1].
Answering Conjunctive Queries with Inequalities inDL-Liteℛ
Authors: Gianluca Cima, Maurizio Lenzerini, Antonella Poggi2782-2789
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that in the two cases, query answering is decidable, and we provide tight complexity bounds for the problem, both for data and combined complexity. |
| Researcher Affiliation | Academia | 1Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Universit a di Roma 2Dipartimento di Lettere e Culture Moderne, Sapienza Universit a di Roma EMAIL |
| Pseudocode | Yes | Figure 1: The algorithm Check Good(O, q, F) |
| Open Source Code | No | The paper does not contain any statement about releasing source code, nor does it provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that would use a dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would use training, validation, or test splits. |
| Hardware Specification | No | The paper describes theoretical results and does not report on empirical experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific setup details or hyperparameters. |