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Journal of Integrative Agriculture  2012, Vol. 11 Issue (5): 741-751    DOI: 10.1016/S1671-2927(00)8595
SECTION 2: Theory, Technology and Method Advanced Online Publication | Current Issue | Archive | Adv Search |
Ontology Engineering and Knowledge Services for Agriculture Domain
 Asanee Kawtrakul
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
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摘要  This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS (Agricultural Ontology Services) Vocbench which is the heart of knowledge services in agriculture domain.

Abstract  This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS (Agricultural Ontology Services) Vocbench which is the heart of knowledge services in agriculture domain.
Keywords:  knowledge service      ontology construction      ontology design      ontology maintenance      natural language processing      ontology based knowledge services  
Received: 28 June 2011   Accepted:
Corresponding Authors:  Correspondence Asanee Kawtrakul, E-mail: asanee_naist@yahoo.com     E-mail:  asanee_naist@yahoo.com
About author:  Asanee Kawtrakul, E-mail: asanee_naist@yahoo.com

Cite this article: 

Asanee Kawtrakul. 2012. Ontology Engineering and Knowledge Services for Agriculture Domain. Journal of Integrative Agriculture, 11(5): 741-751.

[1]Bos J. 2005. Towards wide-coverage semantic interpretation. In: Proceedings of the 6th International Workshop on Computational Semantics IWCS-6. IWCS, Tilburg, Netherlands.

[2]Barker K, Szpakowicz S. 1998. Semiautomatic recognition of noun modifier relationships. In: Proceedings of the 17th International Conference on Computational linguistics. Association for Computational Linguistics Stroudsburg, PA, USA. pp. 96-102.

[3]Byrne K. 2006. Gathering cultural data with RDF. In: Proceedings of the Jena Users Conference. Bristol, UK. Chanlekha H, Kawtrakul A. 2004. Thai named entity extraction by incorporating maximum entropy model with simple heuristic information. In: Proceedings of the IJCNLP’ 2004. IJCNLP, Hainan Island, China.

[4]Collier N, Kawazoe A, Jin L, Shigematsu M, Dien D, Barrero R, Takeuchi K, Kawtrakul A. 1999. A Multilingual ontology for infectious disease surveillance rationale, design and challenges. Language Resources and Evaluation, 40, 405-413.

[5]FAO. 1999. AGROVOC: Multilingual Agricultural Thesaurus. FAO, Rome. Hearst M. 1992. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics. Affiliated Organizations, Nantes France. Imsombut A. 2007. Automatic Thai ontology construction from corpus, thesaurus, and dictionary. Ph D thesis, Kasetsart University, Thailand.

[6]Imsombut A, Kawtrakul A. 2005. Semi-automatic semantic relations extraction from Thai noun phrases for ontology learning. In: The 6th Symposium on Natural Language Processing 2005 (SNLP 2005). SNLP, Chiang Rai, Thailand. Imsombut A, Kawtrakul A. 2007. Automatic building of an ontology on the basis of text corpora in Thai. Language Resources and Evaluation, 42, 137-149.

[7]Kawtrakul A, Pechsiri C, Permpool T, Thamvijit D, Sornprasert P, Yingsaeree C, Suktarachan M. 2006. Ontology driven k-portal construction and k-service provision. In: LREC2006 Conference. ACL, Genoa, Italy.

[8]Kawtrakul A, Pechsiri C, Sachit R, Frederic A. 2009.problemssolving map extraction with collective intelligence analysis and language engineering. In:Information Science Reference in Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration. AMAZON, USA.

[9]Kawtrakul A, Sriswasdi W, Wuttilerdcharoenwong S, Khunthong V, Frederic A S L, Decha J, Werachai N, Anan P. 2008. Cyberbrain: Towards the next generation social intelligence. In: IAALD AFITA WCCA 2008. ACM, Tokyo, Japan.

[10]Kawtrakul A, Imsombut A. 2010. Agricultural ontology construction and maintenance in Thai. In: Huang C R, Calzolari N, Gangemi A, Lenci A, eds., Ontology and the Lexicon: A Natural Language Processing Perspective. Cambridge University Press, USA.

[11]Kietz J U, Maedche A, Volz R. 2000. A method for semiautomatic ontology acquisition from a corporate intranet. In: Proceedings of Workshop Ontologies and Text, EKAW’2000. Springer, France.

[12]Landau M F, Morin E.1999. Extracting semantic relationships between terms: supervised vs. unsupervised methods. In: Proceedings of the International Workshop on Ontological Engineering on the Global Information Infrastructure. EKAW, Dagstuhl Castle, Germany. Maedche A, Staab S. 2001. Ontology learning for the semantic Web. IEEE Intelligent Systems, 16, 72-79.

[13]Navigli R, Velardi P, Gangemi A. 2003. Ontology learning and its application to automated terminology translation. IEEE Intelligent Systems, 18, 22-31.

[14]Pechsiri C, Kawtrakul A. 2007. Mining causality from texts for question answering system. In: The Institute of Electronics, Information and Communication Engineers(IEICE), IEICE Transactions. Oxford University Press, UK. pp. 1523-1533.

[15]Riedel S, Klein E. 2005. Genic interaction extraction with semantic and syntactic chains. In: Proceedings of the Learning in Logic Workshop, ICML 2005. ICML, Bonn, Germany. Schutz A, Buitelaar P. 2005. RelExt: a tool for relation extraction from text in ontology extension. In: Proceedings of the International Semantic Web Conference. ISWC, Galway, Ireland.

[16]Soergel D. 2004. The role of thesauri and ontologies for improving knowledge retrieval from web applications. In: Proceedings of International Conference on Electronic Publishing. Brasilia, Brasil. Sousan W, Wylie K, Chen Z G. 2009. Constructing domain ontology from texts: a practical approach and a case Study. In: Proceedings of the 5th International Conference on Next Generation Web Services Practices. IEEE, Prague, Czech Republic.

[17]Suktarachan M, Thamvijit D, Rajbhandari S, Noikongka D, Na Mahasarakram P P, Yongyuth P, Kawtrakul A, Sini M. 2008. Workbench with authoring tools for collaborative multi-lingual ontological knowledge construction and maintenance. In: Proceedings of The 6th International Conference on Language Resources and Evaluation. LREC, Marrakech, Morocco. Thunkijjanukij A. 2009. Ontology development for agricultural research knowledge management: a case study for Thai rice. Ph D thesis, Kasetsart University, Thailand.

[18]Turban E, Leidner D, McLean E, Wetherbe J. 2005. Information Technology for Management. 5th ed., Wiley and Son, Toronto, Canada. Vanderwende L.1994. Algorithm for automatic interpretation of noun sequences. In: Proceedings of the 15th Conference on Computational Linguistics, Association for Computational Linguistics. Morristown, NJ, USA. pp. 782-788.
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