A Journey into Ontology Approximation: From Non-Horn to Horn

Authors: Anneke Haga, Carsten Lutz, Johannes Marti, Frank Wolter

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We perform a case study based on the Manchester ontology corpus that confirm this expectation. We also show that if OS is an acyclic ELU ontology, then a finite EL approximation always exists (though it need not be acyclic). We have considered the seven non-trivial ELU ontologies that are part of the Manchester OWL corpus. The size of the ontologies ranges from 113 to 813 concept inclusions and equalities. All ontologies use disjunction on the right-hand side of CIs (thus in a non-trivial way) and none of them is acyclic. We have been able to prove that all these ontologies are finitely generating and thus the approximation Oω T is finite.
Researcher Affiliation Academia Anneke Haga1 , Carsten Lutz1 , Johannes Marti2 and Frank Wolter3 1Universit at Bremen, Germany 2Universiteit van Amsterdam, Netherlands 3University of Liverpool, UK
Pseudocode Yes Figure 1: Candidate EL approximation OT . Figure 2: ℓ-bounded EL approximation Oℓ T . Figure 3: ℓ-bounded EL approximation Oℓ T .
Open Source Code No The paper does not provide an unambiguous statement of releasing source code or a direct link to a code repository for the methodology described.
Open Datasets Yes We perform a case study based on the Manchester ontology corpus that confirm this expectation. 1http://owl.cs.manchester.ac.uk/publications/supportingmaterial/owlcorpus/
Dataset Splits No The paper does not provide specific training/test/validation dataset splits or methodologies for data partitioning.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its analysis or case study.
Software Dependencies No The paper does not specify any software dependencies with version numbers needed to replicate the work.
Experiment Setup No The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings.