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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |