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].
Towards Universal Languages for Tractable Ontology Mediated Query Answering
Authors: Heng Zhang, Yan Zhang, Jia-Huai You, Zhiyong Feng, Guifei Jiang3049-3056
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus on three families of tractable OMQA-languages, including ๏ฌrst-order rewritable languages and languages whose data complexity of the query answering is in AC0 or PTIME. On the negative side, we prove that there is, in general, no universal language for each of these families of languages. On the positive side, we propose a novel property, the locality, to approximate the ๏ฌrst-order rewritability, and show that there exists a language of disjunctive embedded dependencies that is universal for the family of OMQA-languages with locality. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, Australia 3School of Computer Science and Technology, Huazhong University of Technology and Science, Wuhan, China 4Department of Computing Science, University of Alberta, Edmonton, Canada 5College of Software, Nankai University, Tianjin, China |
| Pseudocode | Yes | Procedure 1: Generating Sequences t and N |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. It discusses abstract 'databases' and 'instances' within a logical framework, not concrete, publicly available datasets for machine learning. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe the implementation of any software. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, no experimental setup details such as hyperparameters or training settings are provided. |