Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies

Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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.