Provenance for the Description Logic ELHr
Authors: Camille Bourgaux, Ana Ozaki, Rafael Penaloza, Livia Predoiu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Detailed proofs are available in [Bourgaux et al., 2020]. The paper focuses on theoretical contributions: proposing a framework, defining semantics, presenting algorithms, proving theorems about complexity, and analyzing properties of description logics with provenance. It uses examples to illustrate concepts, but these are not empirical evaluations on datasets. No performance metrics, no training/test/validation splits, no baselines from experiments. |
| Researcher Affiliation | Academia | 1DI ENS, ENS, CNRS, PSL University & Inria, Paris, France 2University of Bergen, Norway 3University of Milano-Bicocca, Italy 4Free University of Bozen-Bolzano, Italy |
| Pseudocode | Yes | Table 1: Completion rules. A, . . . , D NC { }, R, S, Ri NR, m, mi NM. |
| Open Source Code | No | The paper only refers to proofs being available in an arXiv paper, not source code for the methodology. No statement about releasing code for the work described. 'Detailed proofs are available in [Bourgaux et al., 2020]. ar Xiv:2001.07541 [cs.LO].' |
| Open Datasets | No | The paper is theoretical and does not use or refer to publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup or hyperparameters. |