Extracting a justification for OWL ontologies by critical axioms

Abstract

Extracting justifications for web ontology language (OWL) ontologies is an important mission in ontology engineering. In this paper, we focus on black-box techniques which are based on ontology reasoners. Through creating a recursive expansion procedure, all elements which are called critical axioms in the justification are explored one by one. In this detection procedure, an axiom selection function is used to avoid testing irrelevant axioms. In addition, an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests w.r.t. satisfiability. It is implemented by employing a pseudo model to detect “obvious” satisfiability directly. The experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.

Keywords

description logics / automated reasoning / ontology engineering / justification

References

[1] Schlobach S, Cornet R. Non-standard reasoning services for the debugging of description logic terminologies. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003, 355–362

[2] Kalyanpur A, Parsia B, Horridge M, Sirin E. Finding all justifications of OWL DL entailments. In: Proceedings of the 6th International the Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference. 2007, 267–280

[3] Schlobach S, Huang Z S, Cornet R, Harmelen F V. Debugging incoherent terminologies. Journal of Automated Reasoning, 2007, 39(3): 317–349.

[4] Baader F, Penaloza R. Automata-based axiom pinpointing. Journal of Automated Reasoning, 2010, 45(2): 91–129

[5] Ma Y, Penaloza R. Towards parallel repair: an ontology decomposition-based approach. In: Proceedings of the 27th International Workshop on Description Logics. 2014, 633–645

[6] Teymourlouie M, Zaeri A, Nematbakhsh M A, Staab S. Detecting hidden errors in an ontology using contextual knowledge. Expert Systems with Applications, 2018, 95: 312–323

[7] Xue H, Qin B, Liu T. Topic hierarchy construction from heterogeneous evidence. Frontiers of Computer Science, 2016, 10(1): 136–146

[8] Wu L, Su K, Han Y. Reasoning about knowledge, belief and certainty in hierarchical multi-agent systems. Frontiers of Computer Science, 2017, 11(3): 499–510

[9] Ouyang D, Cui X, Ye Y. Integrity constraints in OWL ontologies based on grounded circumscription. Frontiers of Computer Science, 2013, 7(6): 812–821

[10] Zhang Y, Ouyang D, Ye Y. Glass box debugging algorithm based on unsatisfiable dependent paths. IEEE Access, 2017, 5: 18725–18736

[11] Zhang Y, Ouyang D, Ye Y. An optimization strategy for debugging incoherent terminologies in dynamic environments. IEEE Access, 2017, 5: 24284–24300

[12] Friedrich G, Shchekotykhin K M. A general diagnosis method for ontologies. In: Proceedings of the 4th International Conference on the Semantic Web. 2005, 232–246

[13] Schlobach S. Diagnosing terminologies. In: Proceedings of the 20th National Conference on Artificial Intelligence. 2005, 670–675

[14] Shchekotykhin K M, Friedrich G, Fleiss P, Rodler P. Interactive ontology debugging: two query strategies for efficient fault localization. Journal of Web Semantics, 2012, 12: 88–103

[15] Jannach D, Schmitz T, Shchekotykhin K M. Parallel model-based diagnosis on multi-core computers. Journal of Artificial Intelligence Research, 2016, 55: 835–887

[16] Horridge M, Bauer J, Parsia B, Sattler U. Understanding entailments in OWL. In: Proceedings of the 5th OWLED Workshop on OWL. 2008, 26–27

[17] Bail S, Parsia B, Sattler U. Declutter your justifications: determining similarity between OWL explanations. In: Proceedings of the 1st International Workshop on Debugging Ontologies and Ontology Mappings. 2012, 13–24

[18] Reiter R. A theory of diagnosis from first principles. Artificial Intelligence, 1987, 32: 57–95

[19] Maaren H V,Wieringa S. Finding guaranteed MUSes fast. In: Proceedings of the 11th International Conferences on Theory and Applications of Satisfiability Testing. 2008, 291–304

[20] Kullmann O, Lynce I, Marques J. Categorisation of clauses in conjunctive normal forms: minimally unsatisfiable sub-clause-sets and the lean kernel. In: Proceedings of the 9th International Conference of Theory and Applications of Satisfiability Testing. 2006, 22–35

[21] Huang Z S, Harmelen F V, Teije A Y. Reasoning with inconsistent ontologies: framework, prototype, and experiment. In: Davies J, Studer R,Warren P, eds. SemanticWeb Technologies: Trends and Research in Ontology-based Systems. Hoboken: John Wiley & Sons, Inc., 2006

[22] Horrocks I. Implementation and optimisation techniques. In: Baader F, Calvanese D, McGuinness D, Nardi D, Schneider P F, eds. The Description Logic Handbook: Theory, Implementation, and Applications. 2nd ed. London: Cambridge University Press, 2007

[23] Parsia B, Halaschek C, Sirin E. Towards incremental reasoning through updates in OWL-DL. In: Proceedings of the 15th International Conference of World Wide Web. 2006

[24] Grau B C, Halaschek C, Kazakov Y, Suntisrivaraporn B. Incremental classification of description logics ontologies. Journal of Automated Reasoning, 2010, 44(4): 337–369

[25] Horrocks I, Schneider P F. Reducing OWL entailment to description logic satisfiability. In: Proceedings of the 2nd International Conference on the Semantic Web. 2003, 17–29

[26] Haarslev V, Moller R, Turhan A Y. Exploiting pseudo models for TBox and ABox reasoning in expressive description logics. In: Proceedings of International Joint Conference on Automated Reasoning. 2001, 61–75

[27] Ji Q, Gao Z, Huang Z, Zhu M. Measuring effectiveness of ontology debugging systems. Knowledge-Based Systems, 2014, 71: 169–186

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