Efficient Dependency Analysis for Rule-Based Ontologies

TL;DR

This paper develops efficient algorithms for analyzing dependencies in rule-based ontologies, enabling practical applications like ensuring redundancy-free knowledge graphs in large-scale real-world ontologies.

Contribution

It introduces optimized algorithms for positive reliances and restraints, making dependency analysis scalable and practical for large ontologies.

Findings

Algorithms scale to over 100,000 rules in real-world ontologies

Enables practical analysis of redundancy in knowledge graphs

Supports applications in ontology-based query answering

Abstract

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.