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Conference Papers Year : 2023

Efficient Extraction of EL-Ontology Deductive Modules

Abstract

Because widely used real-world ontologies are often complex and large, one important challenge has emerged: designing tools for users to focus on sub-ontologies corresponding to their specific interests. To this end, various modules have been introduced to provide concise ontology views. This work concentrates on extracting deductive modules that preserve logical entailment over a given vocabulary. Existing deductive module proposals are either inefficient from a computing point of view or unsatisfactory from a quality point of view because the modules extracted are not concise enough. For example, minimal modules guarantee the most concise results, but their computation is highly time-consuming, while ⊥⊤∗-modules are easy to compute but usually contain many redundant items. To overcome computation cost and lack of quality, we propose to compute two kinds of deductive modules called pseudo-minimal modules and complete modules for EL-ontology. Our deductive module definitions rely on associating a tree representation with an ontology, and their computation is based on SAT encoding. Our experiments on real-world ontologies show that our pseudo-minimal modules are indeed minimal modules in almost all cases (98.9%), and computing pseudo-minimal modules is more efficient (99.79 times faster on average) than the state-of-the-art method Zoom for computing minimal modules. Also, our complete modules are more compact than ⊥⊤∗-modules, but their computation time remains comparable. Finally, note that our proposal applies to EL-ontologies while Zoom only works for EL-terminologies.

Dates and versions

hal-04247184 , version 1 (18-10-2023)

Identifiers

Cite

Hui Yang, Yue Ma, Nicole Bidoit. Efficient Extraction of EL-Ontology Deductive Modules. AAAI, Feb 2023, Washington DC, United States. pp.6575-6582, ⟨10.1609/aaai.v37i5.25808⟩. ⟨hal-04247184⟩
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