Advertisement

Automatic Ontology Development from Semi-structured Data in Web-Portal: Towards Ontology of Thai Rice Knowledge

  • Taneth RuangrajitpakornEmail author
  • Rachada Kongkachandra
  • Pokpong Songmuang
  • Thepchai Supnithi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)

Abstract

Heavyweight ontology is difficult to develop even for experienced ontology engineer, but it is required for semantic based computer software as core knowledge. Most of existing automated ontology development methods however focuses on lightweight ontology, taxonomy-instance extraction. This work presents a method to automatically construct relation-heavy ontology from semi-structured web content providing deep knowledge in specific domain. Classes, instances and hierarchical relation are derived from the category content from the web. Relations are extracted based on frequent expression details. Templates of relation and its range are extracted from common content with partial difference. Similar contexts are grouped with similarity and form as relation to attach to ontology classes. The case study of this work is Thai rice knowledge including rice variety, disease, weed and pest provided in website from responsible government. The complete ontology is used as core knowledge for personalised web service. The service assists in filter content in summary that matched to users’ information. Courtesy to the generated relation-heavy ontology, it is able to recommend relevant chained concepts to users based on semantic relation. From evaluation from an expert, the generated ontology obtained about 97% accuracy.

Keywords

Ontology learning Knowledge extraction Pattern-based detection Textual template Semi-structured content 

References

  1. 1.
    Studer, R., Benjamins, R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–198 (1998)CrossRefGoogle Scholar
  2. 2.
    Swartout, W., Tate, A.: Ontologies. IEEE Intell. Syst. 14(1), 18–19 (1999)CrossRefGoogle Scholar
  3. 3.
    Uschold, M.: Ontologies and semantics for seamless connectivity. SIGMOD Rec. 33(4), 58–64 (2004)CrossRefGoogle Scholar
  4. 4.
    Smith, B.: Beyond concepts: ontology as reality representation. In: Varzi, A.C., Vieu, L. (eds.) Formal Ontology in Information Systems – Proceedings of the Third International Conference (FOIS 2004), pp. 73–85. IOS Press, Amsterdam (2004)Google Scholar
  5. 5.
    Miller, G.: WordNet: An electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  6. 6.
    Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  7. 7.
    Davies, J.: Lightweight ontologies. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology: Computer Applications, pp. 197–229. Springer, Dordrecht (2010).  https://doi.org/10.1007/978-90-481-8847-5_9CrossRefGoogle Scholar
  8. 8.
    Mizoguchi, R.: Tutorial on ontological engineering—part 1: introduction to ontological engineering. New Gener. Comput. 21(4), 365–384 (2003)CrossRefGoogle Scholar
  9. 9.
    Reimer, U., Maier, E., Streit, S., Diggelmann, T., Hoffleisch, M.: Learning a lightweight ontology for semantic retrieval in patient-centered information systems. Int. J. Knowl. Manag. 7(3), 11–26 (2011)CrossRefGoogle Scholar
  10. 10.
    Faure, D., Nédellec, C.: Knowledge acquisition of predicate argument structures from technical texts using machine learning: the system Asium. In: Fensel, D., Studer, R. (eds.) EKAW 1999. LNCS (LNAI), vol. 1621, pp. 329–334. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48775-1_22CrossRefGoogle Scholar
  11. 11.
    Suchanek, F., Kasneci, G., Weikum, G.; Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2004)Google Scholar
  12. 12.
    Gruber, T.R.: Towards principles for the design of ontologies used for knowledge sharing. In: Guarino, N., Poli, R. (eds.) Formal Ontology in Conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers, Deventer (1993)Google Scholar
  13. 13.
    Rector, A., et al.: OWL pizzas: practical experience of teaching OWL-DL: common errors & common patterns. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS, vol. 3257, pp. 63–81. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30202-5_5CrossRefGoogle Scholar
  14. 14.
    Sabou, M., Wroe, C., Goble, C., Mishne, G.: Learning domain ontologies for web service descriptions: an experiment in bioinformatics. In: Proceedings of the 14th International World Wide Web Conference (WWW 2005), Chiba, Japan (2005)Google Scholar
  15. 15.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. JAIR – J. AI Res. 24, 305–339 (2005)CrossRefGoogle Scholar
  16. 16.
    Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005).  https://doi.org/10.1007/11428817_21CrossRefGoogle Scholar
  17. 17.
    Karoui, L., Aufaure, M., Bennacer, N.: Ontology discovery from web pages: application to tourism. In: Proceedings of ECML/PKDD 2004: Knowledge Discovery and Ontologies (KDO 2004) (2004)Google Scholar
  18. 18.
    Davulcu, H., Vadrevu, S., Nagarajan, S., Ramakrishnan, I.: OntoMiner: bootstrapping and populating ontologies from domain specific web sites. IEEE Intell. Syst. 18(5), 24–33 (2003)CrossRefGoogle Scholar
  19. 19.
    Thai. http://www.brrd.in.th/rkb/. Accessed 12 Aug 2016
  20. 20.
    Vijaymeena, M., Kavitha, K.: A survey on similarity measures in text mining. Mach. Learn. Appl.: Int. J. (MLAIJ) 3(1), 19–28 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Taneth Ruangrajitpakorn
    • 2
    • 1
    Email author
  • Rachada Kongkachandra
    • 1
  • Pokpong Songmuang
    • 1
  • Thepchai Supnithi
    • 2
  1. 1.Department of Computer Science, Faculty of Science and TechnologyThammasat UniversityPathumthaniThailand
  2. 2.Language and Semantic Technology LaboratoryNECTECPathumthaniThailand

Personalised recommendations