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Evaluating Datalog Tools for Meta-reasoning over OWL 2 QL

Haya Majid Qureshi, Wolfgang Faber

TL;DR

This work evaluates the practicality of solving meta-querying over OWL 2 QL using the MSER reduction to Datalog. By extending prior tests to nine back-ends across LUBM and MODEUS datasets with both tight and generous resource limits, it reveals that DLV2 and XSB deliver the most robust performance, while RDFox, LogicBlox, and some ASP/heavier hybrid tools encounter scalability challenges on MODEUS. The study provides nuanced insights into how tool characteristics like magic-set optimization, tabling, and grounding affect performance in meta-modeling contexts. The results offer a practical validation of the Datalog-based MSER approach for sizeable ontologies and point to hybrid reasoning and further back-end tuning as promising future directions.

Abstract

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime (DSER) uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015) and Lenzerini et al. (2020); Cima et al. (2017). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi & Faber (2021) by a more detailed experimental analysis and more background. Under consideration in Theory and Practice of Logic Programming (TPLP).

Evaluating Datalog Tools for Meta-reasoning over OWL 2 QL

TL;DR

This work evaluates the practicality of solving meta-querying over OWL 2 QL using the MSER reduction to Datalog. By extending prior tests to nine back-ends across LUBM and MODEUS datasets with both tight and generous resource limits, it reveals that DLV2 and XSB deliver the most robust performance, while RDFox, LogicBlox, and some ASP/heavier hybrid tools encounter scalability challenges on MODEUS. The study provides nuanced insights into how tool characteristics like magic-set optimization, tabling, and grounding affect performance in meta-modeling contexts. The results offer a practical validation of the Datalog-based MSER approach for sizeable ontologies and point to hybrid reasoning and further back-end tuning as promising future directions.

Abstract

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime (DSER) uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015) and Lenzerini et al. (2020); Cima et al. (2017). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi & Faber (2021) by a more detailed experimental analysis and more background. Under consideration in Theory and Practice of Logic Programming (TPLP).
Paper Structure (43 sections, 7 equations, 6 figures, 10 tables)

This paper contains 43 sections, 7 equations, 6 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Outline.
  4. Preliminaries
  5. OWL 2 QL
  6. Syntax.
  7. Meta-modeling Semantics
  8. The query language
  9. Datalog
  10. Syntax.
  11. Semantics
  12. Overview of the Approach
  13. Experimental Setup
  14. Tools
  15. Datalog
  16. ...and 28 more sections

Figures (6)

  • Figure 1: Experiment 1 LUBM with Standard Queries and Simple Meta-Queries
  • Figure 2: Experiment 1 LUBM-ext with Meta Queries
  • Figure 3: Experiment 1 MODEUS with Meta Queries
  • Figure 4: Experiment 2 LUBM with Standard Queries and Simple Meta-Queries
  • Figure 5: Experiment 2 LUBM-ext with Meta Queries
  • ...and 1 more figures

Theorems & Definitions (1)

  • Example 1