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..
Learning Description Logic Concepts: When can Positive and Negative Examples be Separated?
Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz, Hadrien Pulcini, Frank Wolter
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fundamental question of when a separating DL concept exists and provide useful model-theoretic characterizations as well as complexity results for the associated decision problem. For expressive DLs such as ALC and ALCQI, our characterizations show a surprising link to the evaluation of ontology-mediated conjunctive queries. We exploit this to determine the combined complexity (between EXPTIME and NEXPTIME) and data complexity (second level of the polynomial hierarchy) of separability. |
| Researcher Affiliation | Academia | Maurice Funk1 , Jean Christoph Jung1 , Carsten Lutz1 , Hadrien Pulcini2 and Frank Wolter2 1Department of Computer Science, University of Bremen 2Department of Computer Science, University of Liverpool |
| Pseudocode | No | The paper provides theoretical characterizations and complexity analysis but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses various DL concept learning systems (e.g., DL LEARNER) and related work, but it does not provide any statement or link indicating that the authors have released open-source code for their own described methodology. |
| Open Datasets | No | This is a theoretical paper that focuses on complexity analysis and model-theoretic characterizations, and therefore does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper focusing on model-theoretic characterizations and complexity analysis. It does not involve empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper that does not involve empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, as it is a theoretical work on logic and complexity and does not describe software implementations. |
| Experiment Setup | No | As a theoretical paper focused on model-theoretic characterizations and complexity, it does not describe any empirical experimental setup, hyperparameters, or training configurations. |