From Web Resources to Agricultural Ontology： a Method for Semi-Automatic Construction

• Published in: Journal of Integrative Agriculture Vol. 11; no. 5; pp. 775 - 783
• Main Authors: WEI, Yuan-yuan, WANG, Ru-jing, HU, Yi-min, WANG, Xue
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2012; Science Press; School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, P.R.China%Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, P.R.China; Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, P.R.China; Elsevier
• Subjects: agricultural ontology service (AOS); agricultural pest; metadata collection; ontology; ontology agricultural ontology service (AOS) agricultural pest metadata collection; 关系型数据库; 农业信息服务; 农业领域; 半自动; 建设方; 本体论; 知识服务; 网络资源; agricultural pest; agricultural ontology service (AOS); ontology; metadata collection
• ISSN: 2095-3119; 2352-3425
• DOI: 10.1016/S2095-3119(12)60067-7

In recent years, with the rapid development of information science, ontology becomes a popular research topic in the fields of knowledge engineering and information management. The reason for ontology being so popular is in large part due to what they promise: a shared and common understanding of some domain that can be communicated across people and computers. In the field of agriculture, FAO has started up the Agricultural Ontology Service (AOS) study project since 2001, AOS aims at providing knowledge service by agricultural domain ontology, it is the new seedtime for agricultural information service. However, establishing the ontology necessitates a great deal of expert assistance; manually setting it up would entail a lot of time, not to mention that there are only a handful of experts available. For this reason, using automatic technology to construct the ontology is a subject worth pursuing. A semi-automatic construction method for agricultural professional ontology from web resources is presented in this paper. For semi-structured web pages, the method automatically extracted and stored structured data through a program, built pattern mapping between relational database and ontology through human-computer interaction, and automatically generated a preliminary ontology, finally completed checking and refining by domain experts. The method provided a viable approach for ontology construction based on network resources in the actual work.