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A Framework for Syntactic and Semantic Quality Evaluation of Ontologies

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Secure Knowledge Management In The Artificial Intelligence Era (SKM 2021)

Abstract

The increasing focus on Web 3.0 is leading to automated creation and enrichment of ontologies and other linked datasets. Alongside automation, quality evaluation of enriched ontologies can impact software reliability and reuse. Current quality evaluation approaches oftentimes seek to evaluate ontologies in either syntactic (degree of following ontology development guidelines) or semantic (degree of semantic validity of enriched concepts/relations) aspects. This paper proposes an ontology quality evaluation framework consisting of: (a) SynEvaluator and (b) SemValidator for evaluating syntactic and semantic aspects of ontologies respectively. SynEvaluator allows dynamic task-specific creation and updation of syntactic rules at run-time without any need for programming. SemValidator uses Twitter-based expertise of validators for semantic evaluation. The efficacy and validity of the framework is shown empirically on multiple ontologies.

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Notes

  1. 1.

    https://github.com/Remorax/SynEvaluator/.

  2. 2.

    https://github.com/Remorax/SemValidator/.

  3. 3.

    https://synevaluator.herokuapp.com/.

  4. 4.

    http://semvalidator.herokuapp.com/.

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    https://bit.ly/3zfdI8f.

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    https://pewrsr.ch/3vX5q2G.

  7. 7.

    https://bit.ly/3z5IDn9.

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    https://bit.ly/3fSjWmA.

  9. 9.

    https://bit.ly/3pmMc3O.

  10. 10.

    https://bit.ly/3z5lGR1.

  11. 11.

    https://bit.ly/3yYGGZP.

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    https://bit.ly/3iiEtCm.

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    https://bit.ly/3wXRHZj.

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    https://bit.ly/34PIUN9.

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    https://bit.ly/3wXRJQV.

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    https://bit.ly/3pvzbFh.

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    https://bit.ly/3wUa9SA.

References

  1. Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 1–15. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_1

    Chapter  Google Scholar 

  2. Amardeilh, F., Laublet, P., Minel, J.L.: Document annotation and ontology population from linguistic extractions. In: Proceedings of the 3rd International Conference on Knowledge Capture, pp. 161–168 (2005)

    Google Scholar 

  3. Beckett, D., Berners-Lee, T., Prud’hommeaux, E., Carothers, G.: RDF 1.1 turtle. In: World Wide Web Consortium, pp. 18–31 (2014)

    Google Scholar 

  4. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)

    Article  Google Scholar 

  5. Burton-Jones, A., Storey, V.C., Sugumaran, V., Ahluwalia, P.: A semiotic metrics suite for assessing the quality of ontologies. Data Knowl. Eng. 55(1), 84–102 (2005)

    Article  Google Scholar 

  6. Cer, D., et al.: Universal Sentence Encoder. arXiv preprint arXiv:1803.11175 (2018)

  7. Dividino, R.Q., Romanelli, M., Sonntag, D., et al.: Semiotic-based ontology evaluation tool (S-OntoEval). In: LREC (2008)

    Google Scholar 

  8. Drummond, N.: Stanford pizza ontology. https://protege.stanford.edu/ontologies/pizza/pizza.owl. Accessed 28 Mar 2021

  9. Duque-Ramos, A., et al.: Evaluation of the OQuaRE framework for ontology quality. Expert Syst. Appl. 40, 2696–2703 (2013)

    Article  Google Scholar 

  10. Fenz, S., Goluch, G., Ekelhart, A., Riedl, B., Weippl, E.: Information security fortification by ontological mapping of the ISO/IEC 27001 standard. In: 13th Pacific Rim International Symposium on Dependable Computing (PRDC 2007), pp. 381–388. IEEE (2007)

    Google Scholar 

  11. Gangemi, A., Catenacci, C., Ciaramita, M., Lehmann, J.: A theoretical framework for ontology evaluation and validation. In: SWAP, vol. 166, p. 16 (2005)

    Google Scholar 

  12. Gómez-Pérez, A.: Evaluation of taxonomic knowledge in ontologies and knowledge bases (1999)

    Google Scholar 

  13. Guarino, N., Welty, C.: Evaluating ontological decisions with OntoClean. Commun. ACM 45(2), 61–65 (2002)

    Article  Google Scholar 

  14. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)

    Google Scholar 

  15. Iyer, V., Mohan, L., Bhatia, M., Reddy, Y.R.: A survey on ontology enrichment from text. In: Proceedings of the 16th International Conference on Natural Language Processing, pp. 95–104. NLP Association of India, International Institute of Information Technology, Hyderabad, India, December 2019. https://aclanthology.org/2019.icon-1.11

  16. Kiptoo, C.C.: Ontology enhancement using crowdsourcing: a conceptual architecture. Int. J. Crowd Sci. (2020)

    Google Scholar 

  17. Kontokostas, D., Zaveri, A., Auer, S., Lehmann, J.: TripleCheckMate: a tool for crowdsourcing the quality assessment of linked data. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2013. CCIS, vol. 394, pp. 265–272. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41360-5_22

    Chapter  Google Scholar 

  18. Lantow, B.: OntoMetrics: putting metrics into use for ontology evaluation. In: KEOD, pp. 186–191 (2016)

    Google Scholar 

  19. Lozano-Tello, A., Gómez-Pérez, A.: OntoMetric: a method to choose the appropriate ontology. J. Database Manag. 2, 1–18 (2004)

    Article  Google Scholar 

  20. Maedche, A., Staab, S.: Ontology learning for the semantic web. Intell. Syst. 16(2), 72–79 (2001)

    Article  Google Scholar 

  21. Makki, J., Alquier, A.M., Prince, V.: An NLP-based ontology population for a risk management generic structure. In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, pp. 350–355 (2008)

    Google Scholar 

  22. McDaniel, M., Storey, V.C., Sugumaran, V.: Assessing the quality of domain ontologies: metrics and an automated ranking system. Data Knowl. Eng. 115, 32–47 (2018)

    Article  Google Scholar 

  23. Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: a guide to creating your first ontology (2001)

    Google Scholar 

  24. Noy, N.F., Mortensen, J., Musen, M.A., Alexander, P.R.: Mechanical Turk as an ontology engineer? using microtasks as a component of an ontology-engineering workflow. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 262–271 (2013)

    Google Scholar 

  25. Pittet, P., Barthélémy, J.: Exploiting users’ feedbacks: towards a task-based evaluation of application ontologies throughout their lifecycle. In: International Conference on Knowledge Engineering and Ontology Development, vol. 2 (2015)

    Google Scholar 

  26. Poveda, M.: Catalogue of common pitfalls. http://oops.linkeddata.es/catalogue.jsp. Accessed 28 Mar 2021

  27. Poveda-Villalón, M., Gómez-Pérez, A., Suárez-Figueroa, M.C.: OOPS!: a pitfall-based system for ontology diagnosis. In: Innovations, Developments, and Applications of Semantic Web and Information Systems, pp. 120–148. IGI Global (2018)

    Google Scholar 

  28. Poveda-Villalón, M., Suárez-Figueroa, M.C., Gómez-Pérez, A.: Validating ontologies with OOPS! In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 267–281. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33876-2_24

    Chapter  Google Scholar 

  29. Richard, E., Ralph, H., Johnson, R., et al.: Design patterns: elements of reusable object-oriented software (1995)

    Google Scholar 

  30. Sanagavarapu, L.M., Iyer, V., Reddy, Y.R.: OntoEnricher: a deep learning approach for ontology enrichment from unstructured text. arXiv preprint arXiv:2102.04081 (2021)

  31. Schober, D., Tudose, I., Svatek, V., Boeker, M.: Ontocheck: verifying ontology naming conventions and metadata completeness in protégé 4. J. Biomed. Semant. 3, 1–10 (2012)

    Article  Google Scholar 

  32. Tartir, S., Arpinar, I.B., Sheth, A.P.: Ontological evaluation and validation. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology: Computer Applications. Springer, Dordrecht (2010). https://doi.org/10.1007/978-90-481-8847-5_5

  33. Tibaut, A.: Ontology Evaluation. https://github.com/atibaut/ontology-evaluation. Accessed 15 Mar 2021

  34. Vrandečić, D.: Ontology evaluation. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 293–313. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_13

    Chapter  Google Scholar 

  35. Wohlgenannt, G., Sabou, M., Hanika, F.: Crowd-based ontology engineering with the ucomp protégé plugin. Semant. Web 7(4), 379–398 (2016)

    Article  Google Scholar 

  36. Wong, W., Liu, W., Bennamoun, M.: Ontology learning from text: a look back and into the future. ACM Comput. Surv. (CSUR) 44(4), 1–36 (2012)

    Article  Google Scholar 

  37. Zhang, Y., Saberi, M., Chang, E.: Semantic-based lightweight ontology learning framework: a case study of intrusion detection ontology. In: Proceedings of the International Conference on Web Intelligence, pp. 1171–1177 (2017)

    Google Scholar 

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Iyer, V., Sanagavarapu, L.M., Raghu Reddy, Y. (2022). A Framework for Syntactic and Semantic Quality Evaluation of Ontologies. In: Krishnan, R., Rao, H.R., Sahay, S.K., Samtani, S., Zhao, Z. (eds) Secure Knowledge Management In The Artificial Intelligence Era. SKM 2021. Communications in Computer and Information Science, vol 1549. Springer, Cham. https://doi.org/10.1007/978-3-030-97532-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-97532-6_5

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