Rules for Ontology Population from Text of Malaysia Medicinal Herbs Domain

  • Zaharudin Ibrahim
  • Shahrul Azman Noah
  • Mahanem Mat Noor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)


The primary goal of ontology development is to share and reuse domain knowledge among people or machines. This study focuses on the approach of extracting semantic relationships from unstructured textual documents related to medicinal herb from websites and proposes a lexical pattern technique to acquire semantic relationships such as synonym, hyponym, and part-of relationships. The results show of nine object properties (or relations) and 105 lexico-syntactic patterns have been identified manually, including one from the Hearst hyponym rules. The lexical patterns have linked 7252 terms that have the potential as ontological terms. Based on this study, it is believed that determining the lexical pattern at an early stage is helpful in selecting relevant term from a wide collection of terms in the corpus. However, the relations and lexico-syntactic patterns or rules have to be verified by domain expert before employing the rules to the wider collection in an attempt to find more possible rules.


Knowledge management and extraction medicinal herb semantic web Natural Language Processing knowledge engineering 


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  1. 1.
    Haase, P., Sure, Y.: State-of-the-Art on Ontology Evolution. Institute AIFB, University of Karlsruhe (2004),
  2. 2.
    Staab, S., Schnurr, H.-P., Studer, R., Sure, Y.: Knowledge processes and ontologies. IEEE Intelligent Systems, Special Issue on Knowledge Management 16(1), 26–34 (2001)Google Scholar
  3. 3.
    Fuller, S., Revere, D., Bugni, P.F., Martin, G.M.: A knowledgebase system to enhance scientific discovery: Telemakus. Biomedical Digital Libraries 1, 2 (2004)CrossRefGoogle Scholar
  4. 4.
    Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial Intelligence 91(2), 183–203 (1997)MATHCrossRefGoogle Scholar
  5. 5.
    Alani, H., Kim, S., Millard, D.E., Weal, M.J., Hall, W., Lewis, P.H., Shadbolt, N.R.: Automatic Ontology-Based Knowledge Extraction from Web Documents. IEEE Intelligent Systems 18(1), 14–21 (2003)CrossRefGoogle Scholar
  6. 6.
    Cimiano, P., Hotho, A., Staab, S.: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. Journal of Artificial Intelligence Research 24, 305–339 (2005)MATHGoogle Scholar
  7. 7.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, pp. 539–545 (1992)Google Scholar
  8. 8.
    Kawtrakul, A., Suktarachan, M., Imsombut, A.: Automatic Thai Ontology Construction and Maintenance System. In: Workshop on Papillon, Grenoble, France (2004)Google Scholar
  9. 9.
  10. 10.
    Imsombut, A., Kawtrakul, A.: Automatic building of an ontology on the basis of text corpora in Thai. Journal of Language Resources and Evaluation 42(2), 137–149 (2007)CrossRefGoogle Scholar
  11. 11.
    Zaharudin, I., Noah, S.A., Noor, M.M.: Knowledge Acquisition from Textual Documents for the Construction of Medicinal herb Ontology Domain. J. Applied Science 9(4), 794–798 (2009)CrossRefGoogle Scholar
  12. 12.
    Moldovan, D., Girju, R., Rus, V.: Domain-specific knowledge acquisition from text. In: Proceedings of the sixth conference on Applied natural language processing, Seattle, Washington, pp. 268–275 (2000)Google Scholar
  13. 13.
    Pantel, P., Pennacchiotti, M.: Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 113–120 (2006)Google Scholar
  14. 14.
    Maedche, A., Staab, S.: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2) (2001)Google Scholar
  15. 15.
    Xu., F., Kurz., D., Piskorski., J., Schmeier, S.: A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping. In: Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC 2002), Las Palmas, Canary Islands, Spain, May 29-31 (2002)Google Scholar
  16. 16.
    Girju, R., Badulescu, A., Moldovan, D.: Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations. In: The Proceedings of the Human Language Technology Conference, Edmonton, Canada (2003)Google Scholar
  17. 17.
  18. 18.
    Celjuska, D., Vargas-Vera, M.: Ontosophie: A semi-automatic system for ontology population from text. In: Proceedings of the 3rd International Conference on Natural Language Processing, ICON (2004)Google Scholar
  19. 19.
    Ralph, M.W., Norman, K.S.: Meta-rules as a basis for processing ill-formed input. Computational Linguistics 9(3-4) (1983)Google Scholar
  20. 20.
    Zagibalov., T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zaharudin Ibrahim
    • 1
    • 2
  • Shahrul Azman Noah
    • 2
  • Mahanem Mat Noor
    • 3
  1. 1.Department of Information System Management, Faculty of Information ManagementUniversiti Teknologi MARAShah AlamMalaysia
  2. 2.Department of Information Science, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaSelangorMalaysia
  3. 3.School of Biosciences and Biotechnology, Faculty Science and TechnologyUniversiti Kebangsaan MalaysiaSelangorMalaysia

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