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..
Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies
Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-Lite H, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed in polynomial time. In the dialect DL-Lite F, in contrast, frontiers may be infinite. We identify a natural syntactic restriction that enables the same positive results as for DL-Lite H. We use our results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-Lite H/ restricted DL-Lite F ontology in Angluin s framework of exact learning with only membership queries. |
| Researcher Affiliation | Academia | 1 Leipzig University, Faculty of Mathematics and Computer Science, Germany 2 University of Hildesheim, Institute of Computer Science, Germany |
| Pseudocode | Yes | Algorithm 1 Learning ELIQs under DL-Lite ontologies |
| Open Source Code | No | The paper mentions a long version [Funk et al., 2022] but does not state that source code for the methodology is released or provide a link to it. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no training data is used in the empirical sense. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments that would require dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments, therefore no 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 or hyperparameters are provided. |