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].
Exact Learning of Lightweight Description Logic Ontologies
Authors: Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of learning description logic (DL) ontologies in Angluin et al. s framework of exact learning via queries. We admit membership queries ( is a given subsumption entailed by the target ontology? ) and equivalence queries ( is a given ontology equivalent to the target ontology? ). We present three main results: (1) ontologies formulated in (two relevant versions of) the description logic DL-Lite can be learned with polynomially many queries of polynomial size; (2) this is not the case for ontologies formulated in the description logic EL, even when only acyclic ontologies are admitted; and (3) ontologies formulated in a fragment of EL related to the web ontology language OWL 2 RL can be learned in polynomial time. We also show that neither membership nor equivalence queries alone are sufficient in cases (1) and (3). |
| Researcher Affiliation | Academia | Boris Konev EMAIL Department of Computer Science University of Liverpool, United Kingdom Carsten Lutz EMAIL Department of Computer Science University of Bremen, Germany Ana Ozaki EMAIL Department of Computer Science Dresden University of Technology, Germany Frank Wolter EMAIL Department of Computer Science University of Liverpool, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Na ıve learning algorithm for DL-Lite R Algorithm 2 The learning algorithm for DL-Lite R Algorithm 3 The learning algorithm for DL-Lite R,horn TBoxes Algorithm 4 Function CN-Refine(H, γ) Algorithm 5 Function -Refine(H, γ) |
| Open Source Code | No | The paper mentions "License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v18/16-256.html." This refers to the license for the paper itself and its content, not the release of source code for the methodologies described within the paper. There are no explicit statements or links indicating code availability for the described algorithms. |
| Open Datasets | No | The paper is theoretical, focusing on the learnability and complexity of description logic ontologies. It does not involve empirical studies or experiments that would utilize external datasets. Therefore, there is no mention of publicly available datasets or access information for them. |
| Dataset Splits | No | The paper is theoretical and focuses on the learnability and complexity of description logic ontologies. It does not involve empirical studies or experiments that would utilize datasets, and therefore, no dataset splits are discussed. |
| Hardware Specification | No | The paper is theoretical, presenting algorithms, proofs, and complexity analyses related to description logic ontologies. It does not describe any computational experiments or specify the hardware used for such experiments. |
| Software Dependencies | No | The paper focuses on theoretical aspects of exact learning for description logic ontologies, including algorithms, proofs, and complexity. It does not describe practical implementations of these algorithms or list specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | No | The paper is theoretical, describing a conceptual framework for exact learning of description logic ontologies and presenting algorithms and proofs within this framework. It does not include an experimental setup in the sense of empirical evaluation with hyperparameters, training configurations, or system-level settings. |