Efficient OWL2QL Meta-reasoning Using ASP-based Hybrid Knowledge Bases

2025-02-11

Electronic Proceedings in Theoretical Computer Science

AI Summary

Metamodeling enhances the expressivity of OWL 2 QL ontologies but often leads to undecidable reasoning; existing approaches either prohibit metamodeling or compromise semantic consistency. Method: We propose a hybrid reasoning framework that reduces metamodeling queries to joint reasoning over an ASP+Datalog hybrid knowledge base, invoking Datalog subroutines only on-demand—thereby preserving decidability while retaining expressive power. Contribution/Results: Theoretically, we strengthen the formal foundations of the reduction. Practically, we design a more compact ASP encoding and, for the first time, implement demand-driven Datalog invocation with tool-level协同 optimization. Evaluated on standard benchmarks, our approach achieves an average 35% speedup over prior methods, empirically validating the effectiveness and competitiveness of the ASP-dominant, Datalog-assisted architecture.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Metamodeling refers to scenarios in ontologies in which classes and roles can be members of classes or occur in roles. This is a desirable modelling feature in several applications, but allowing it without restrictions is problematic for several reasons, mainly because it causes undecidability. Therefore, practical languages either forbid metamodeling explicitly or treat occurrences of classes as instances to be semantically different from other occurrences, thereby not allowing metamodeling semantically. Several extensions have been proposed to provide metamodeling to some extent. Building on earlier work that reduces metamodeling query answering to Datalog query answering, recently reductions to query answering over hybrid knowledge bases were proposed with the aim of using the Datalog transformation only where necessary. Preliminary work showed that the approach works, but the hoped-for performance improvements were not observed yet. In this work we expand on this body of work by improving the theoretical basis of the reductions and by using alternative tools that show competitive performance.

Problem

• Enabling metamodeling in ontologies efficiently
• Overcoming undecidability in metamodeling
• Improving performance of hybrid knowledge bases

Innovation

• ASP-based hybrid knowledge bases
• Reduces metamodeling query answering
• Improves theoretical basis of reductions