Efficient Dependency Analysis for Rule-Based Ontologies

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Efficient Dependency Analysis for Rule-Based Ontologies


Alex Ivliev, Larry González, Markus Krötzsch, Stephan Mennicke
Efficient Dependency Analysis for Rule-Based Ontologies
ISWC 2022, to appear
  • KurzfassungAbstract
    Several types of dependencies have been proposed for the static analysis of existential rule ontologies, promising insights about computational properties and possible practical uses of a given set of rules, e.g., in ontology-based query answering. Unfortunately, these dependencies are rarely implemented, so their potential is hardly realised in practice. We focus on two kinds of rule dependencies - positive reliances and restraints - and design and implement optimised algorithms for their efficient computation. Experiments on real-world ontologies of up to more than 100,000 rules show the scalability of our approach, which lets us realise several previously proposed applications as practical case studies. In particular, we can analyse to what extent rule-based bottom-up approaches of reasoning can be guaranteed to yield redundancy-free "lean" knowledge graphs (so-called cores) on practical ontologies.
  • Projekt:Project: CPECInnoSaleScaDS.AI
  • Forschungsgruppe:Research Group: Wissensbasierte Systeme
@inproceedings{IGKM2022,
  author    = {Alex Ivliev and Larry Gonz{\'{a}}lez and Markus Kr{\"{o}}tzsch
               and Stephan Mennicke},
  title     = {Efficient Dependency Analysis for Rule-Based Ontologies},
  booktitle = {ISWC 2022},
  year      = {2022},
  month     = {October}
}