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ComR: a combined OWL reasoner for ontology classification

  • Changlong Wang
  • Zhiyong Feng
  • Xiaowang Zhang
  • Xin Wang
  • Guozheng Rao
  • Daoxun Fu
Research Article
  • 12 Downloads

Abstract

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non-EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the well-known ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).

Keywords

OWL ontology classification reasoner 

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Notes

Acknowledgements

We thank the anonymous referees for their critical comments on a previous version of this paper, which encouraged us to significantly improve the paper. This work was supported by the National Key Research and Development Program of China (2016YFB1000603), the National Natural Science Foundation of China (NSFC) (Grant No. 61672377), and the Key Technology Research and Development Program of Tianjin (16YFZCGX00210). Xiaowang Zhang is supported by Tianjin Thousand Young Talents Program.

Supplementary material

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Supplementary material, approximately 278 KB.

References

  1. 1.
    Horrocks I, Patel-Schneider P F, van Harmelen F. From SHIQ and RDF to OWL: the making of a web ontology language. Journal of Web Semantics, 2003, 1(1): 7–26CrossRefGoogle Scholar
  2. 2.
    Patel-Schneider P, Hayes P, Horrocks I. Web ontology language OWL abstract ayntax and aemantics. W3C Recommendation, 2004Google Scholar
  3. 3.
    Cuenca Grau B, Horrocks I, Motik B, Parsia B, Patel-Schneider P F, Sattler U. OWL 2: the next step for OWL. Journal of Web Semantics, 2008, 6(4): 309–322CrossRefGoogle Scholar
  4. 4.
    Motik B, Patel-Schneider P F, Cuenca Grau B. OWL 2 Web ontology language direct semantics. W3C Recommendation, 2009Google Scholar
  5. 5.
    Berners-Lee T, Hendler J, Lassila O. The semantic Web. Scientific American, 2001, 284(5): 28–37CrossRefGoogle Scholar
  6. 6.
    Sidhu A, Dillon T, Chang E, Sidhu B S. Protein ontology development using OWL. In: Proceedings of the 1st Workshops on OWL: Experiences and Directions. 2005Google Scholar
  7. 7.
    Golbreich C, Zhang S, Bodenreider O. The foundational model of anatomy in OWL: experience and perspectives. Journal of Web Semantics, 2006, 4(3): 181–195CrossRefGoogle Scholar
  8. 8.
    Rector A, Rogers J. Ontological and practical issues in using a description logic to represent medical concept systems: experience from GALEN. In: Proceedings of the 2nd International Summer School on Reasoning Web. 2006, 197–231CrossRefGoogle Scholar
  9. 9.
    Soergel D, Lauser B, Liang A, Fisseha F, Keizer J, Katz S. Reengineering thesauri for new applications: the AGROVOC example. Journal of Digital Information, 2006, 4(4)Google Scholar
  10. 10.
    Derriere S, Richard A, Preite-Martinez A. An ontology of astronomical object types for the virtual observatory. In: Proceedings of the 26th meeting of the IAU on Virtual Observatory in Action: New Science, New Technology, and Next Generation Facilities. 2006Google Scholar
  11. 11.
    Lacy L, Aviles G, Fraser K, Gerber W, Mulvehill A, Gaskill R. Experiences using OWL in military applications. In: Proceedings of the 1st Workshop on OWL: Experiences and Directions. 2005Google Scholar
  12. 12.
    Goodwin J. Experiences of using OWL at the ordnance survey. In: Proceedings of the 1st Workshop on OWL: Experiences and Directions. 2005Google Scholar
  13. 13.
    Lécué F, Schumann A, Sbodio M L. Applying semantic web technologies for diagnosing road traffic congestions. In: Proceedings of the 11th International Semantic Web Conference. 2012, 114–130Google Scholar
  14. 14.
    Lécué F, Tucker R, Bicer V, Tommasi P, Tallevi-Diotallevi S, Sbodio M. Predicting severity of road traffic congestion using semantic web technologies. In: Proceedings of the 11th Extended Semantic Web Conference. 2014, 611–627Google Scholar
  15. 15.
    Kazakov Y, Krötzsch M, Simancík F. Concurrent classification of EL ontologies. In: Proceedings of the 10th International Semantic Web Conference. 2011, 305–320Google Scholar
  16. 16.
    Glimm B, Horrocks I, Motik B, Shearer R, Stoilos G. A novel approach to ontology classification. Journal of Web Semantics, 2011, 14(1): 84–101Google Scholar
  17. 17.
    Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P. The description logic handbook: theory, implementation, and applications. Cambridge: Cambridge University Press, 2007CrossRefMATHGoogle Scholar
  18. 18.
    Kazakov Y. RIQ and SROIQ are harder than SHOIQ. In: Proceedings of the 11th International Conference on Knowledge Representation and Reasoning. 2008, 274–284Google Scholar
  19. 19.
    Horrocks I, Sattler U. A tableau decision procedure for SHOIQ. Journal of Automated Reasoning, 2007, 39(3): 249–276MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Motik B, Shearer R, Horrocks I. Hypertableau reasoning for description logics. Journal of Artificial Intelligence Research, 2009, 36: 165–228MathSciNetMATHGoogle Scholar
  21. 21.
    Glimm B, Horrocks I, Motik B, Stoilos G, Wang Z. HermiT: an OWL 2 reasoner. Journal of Automated Reasoning, 2014, 53(3): 245–269CrossRefMATHGoogle Scholar
  22. 22.
    Tsarkov D, Horrocks I. FaCT++ description logic reasoner:system description. In: Proceedings of the 3rd International Joint Conference on Automated Reasoning. 2006, 292–297Google Scholar
  23. 23.
    Haarslev V, Möller R. Racer System description. In: Proceedings of the 1st International Joint Conference on Automated Reasoning. 2001, 701–705Google Scholar
  24. 24.
    Sirin E, Parsia B, Cuenca Grau B, Kalyanpur A, Katz Y. Pellet: a practical OWL DL reasoner. Journal of Web Semantics, 2007, 5(2): 51–53CrossRefGoogle Scholar
  25. 25.
    Goncalves R S, Parsia B, Sattler U. Performance heterogeneity and approximate reasoning in description logic ontologies. In: Proceedings of the 11th International Semantic Web Conference. 2012, 82–98Google Scholar
  26. 26.
    Krözsch M. OWL 2 profiles: an introduction to lightweight ontology languages. In: Proceedings of the 8th Reasoning Web Summer School. 2012, 112–183Google Scholar
  27. 27.
    Baader F, Brandt S, Lutz C. Pushing the EL envelope. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. 2005, 364–369Google Scholar
  28. 28.
    Harris M A, Clark J, Ireland A. Gene ontology consortium: the gene ontology (GO) database and informatics resource. Nucleic Acids Research, 2004, 32: 258–261CrossRefGoogle Scholar
  29. 29.
    Spackman K A. Rates of change in a large clinical terminology: three years experience with snomed clinical terms. In: Proceedings of the AMIA Annual Symposium. 2005, 714–718Google Scholar
  30. 30.
    Mendez J, Suntisrivaraporn B. Reintroducing CEL as an OWL 2 EL reasoner. In: Proceedings of the 22nd International Workshop on Description Logics. 2009Google Scholar
  31. 31.
    Mendez J. JCel: a modular rule-based reasoner. In: Proceedings of the 1st International Workshop on OWL Reasoner Evaluation. 2012, 858Google Scholar
  32. 32.
    Kazakov Y, Krözsch M, Simancik F. The incredible ELK. Journal of Automated Reasoning, 2014, 53(1): 1–61MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg L J, Eilbeck K, Ireland A, Mungall C J, OBI Consortium, Leontis N, Rocca-Serra P, Ruttenberg A, Sansone S A, Scheuermann R H, Shah N, Whetzel L, Lewis S. The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology, 2007, 25(11): 1251–1255CrossRefGoogle Scholar
  34. 34.
    Sioutos N, De Coronado S, Haber M W, Hartel F W, Shaiu W L, Wright L W. NCI thesaurus: a semantic model integrating cancerrelated clinical and molecular information. Journal of biomedical informatics, 2007, 40: 30–43CrossRefGoogle Scholar
  35. 35.
    Armas Romero A, Cuenca Grau B, Horrocks I. MORe: modular combination of OWL Reasoners for ontology classification. In: Proceedings of the 11th International Semantic Web Conference. 2012, 1–16Google Scholar
  36. 36.
    Tsarkov D, Palmisano I. Divide Et Impera: metareasoning for large ontologies. In: Proceedings of the 9th Internation Workshop on OWL: Experiences and Directions. 2012Google Scholar
  37. 37.
    Song W, Spencer B, Du W. Complete classification of complex ALCHO ontologies using a hybrid reasoning approach. In: Proceedings of the 26th International Workshop on Description Logics. 2013Google Scholar
  38. 38.
    Steigmiller A, Glimm B, Liebig T. Coupling tableau algorithms for expressive description logics with completion-based saturation procedures. In: Proceedings of the 7th International Joint Conference on Automated Reasoning. 2014, 449–463Google Scholar
  39. 39.
    Angeli G, Nayak N, Manning G D. Combining natural logic and shallow reasoning for question answering. Technical Report in The Stanford Natural Language Processing Group, 2016Google Scholar
  40. 40.
    Del Vescovo C, Parsia B, Sattler U, Schneider T. The modular structure of an ontology: atomic decomposition. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 2232–2237Google Scholar
  41. 41.
    Del Vescovo C, Parsia B, Sattler U. Topicality in logic-based ontologies. In: Proceedings of 19th International Conference on Conceptual Structures. 2011, 25–29Google Scholar
  42. 42.
    Del Vescovo C, Parsia B, Sattler U. Logical relevance in ontologies. In: Proceedings of the International Workshop on Description Logics. 2012Google Scholar
  43. 43.
    Klinov P, Del Vescovo C, Schneider T. Incrementally updateable and persistent decomposition of OWL ontologies. In: Proceedings of the 9th International Workshop on OWL: Experiences and Directions. 2012Google Scholar
  44. 44.
    Horridge M, Mortensen J M, Parsia B, Sattler U, Musen M A. A study on the atomic decomposition of ontologies. In: Proceedings of the 13th International Semantic Web Conference. 2014, 65–80Google Scholar
  45. 45.
    Wang C L, Feng Z Y. A novel combination of reasoners for ontology classification. In: Proceedings of the 25th IEEE International Conference on Tools with Artificial Intelligence. 2013, 463–468Google Scholar
  46. 46.
    Cuenca Grau B, Horrocks I, Kazakov Y, Sattler U. Modular reuse of ontologies: theory and practice. Journal of Artificial Intelligence Researchvol, 2008, 31(1): 273–318MathSciNetMATHGoogle Scholar
  47. 47.
    Cuenca Grau B, Halaschek-Wiener C, Kazakov Y, Suntisrivaraporn B. Incremental classification of description logics ontologies. Journal of Automated Reasoning, 2010, 44(4): 337–369MathSciNetCrossRefMATHGoogle Scholar
  48. 48.
    Del Vescovo C, Gessler D D, Klinov P, Parsia B, Sattler U, Schneider T, Winget A. Decomposition and modular structure of bioportal ontologies. In: Proceedings of 10th International Semantic Web Conference. 2011, 130–145Google Scholar
  49. 49.
    Del Vescovo C. The Modular structure of an ontology: atomic decomposition and its applications. Dissertation for the Doctoral Degree. Manchester: The University of Manchester, 2013Google Scholar
  50. 50.
    Simancik F, Kazakov Y, Horrocks I. Consequence-based reasoning beyond horn ontologies. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 1093–1098Google Scholar
  51. 51.
    Martín-Recuerda F, Walther D. Fast modularisation and atomic decomposition of ontologies using axiom dependency hypergraphs. In: Proceedings of 13th International Semantic Web Conference. 2014, 49–64Google Scholar
  52. 52.
    Groot P, Stuckenschmidt H, Wache H. Approximating description logic classification for semantic web reasoning. In: Proceedings of the 2nd European Semantic Web Conference. 2005Google Scholar
  53. 53.
    Kazakov Y. Consequence-driven reasoning for horn SHIQ ontologies. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009, 2040–2045Google Scholar
  54. 54.
    Lembo D, Santarelli V O, Fabio Savo D. A graph-based approach for classifying OWL 2 QL ontologies. In: Proceedings of the 26th International Workshop on Description Logics. 2013Google Scholar
  55. 55.
    Liu Z H, Feng Z Y, Zhang X W, Wang X, Rao G Z. RORS: enhanced rule-based OWL reasoning on Spark. In: Proceedings of the 18th Asia-Pacific Web Conference on Web Technologies and Applications. 2016, 444–448CrossRefGoogle Scholar
  56. 56.
    Liu Z H, Ge W, Zhang X W, Feng Z Y. Enhancing rule-based OWL reasoning on spark. In: Proceedings of the 15th International Semantic Web Conference (Posters & Demonstrations Track). 2016Google Scholar
  57. 57.
    Wang C L, Feng Z Y, Rao G Z, Wang X, Zhang X W. From datalog reasoning to modular structure of an ontology. In: Proceedings of the 14th International Semantic Web Conference (Posters & Demonstrations Track). 2015Google Scholar
  58. 58.
    Armas Romero A, Kaminski M, Cuenca Grau B, Horrocks I. Ontology module extraction via datalog reasoning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 1410–1416Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Changlong Wang
    • 1
    • 2
    • 3
  • Zhiyong Feng
    • 1
    • 2
  • Xiaowang Zhang
    • 1
    • 2
  • Xin Wang
    • 1
    • 2
  • Guozheng Rao
    • 1
    • 2
  • Daoxun Fu
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina
  3. 3.School of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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