AGRIX: An Ontology Based Agricultural Expertise Retrieval Framework

Generally, information retrieval (IR) performs keyword search based on the user query to find a set of relevant documents. In the domain of agricultural expertise retrieval, the goal is to find a group of experts who has knowledge in agriculture (by using publications as the evidence) specified by the input query. Typical publication IR systems could sometimes return the search result sets, which consist of a huge amount of publications. Some of the returned publications are not relevant to the individual user's information need. In this paper, an ontology based Agricultural expertise retrieval framework called AGRIX is proposed with the focus on the ontology creation to cover three following aspects: (1) expert profiles and publications, (2) type of plants and (3) problem solving. To build the ontology model, we used a set of publications (1,249 records) which was collected from the Thai national AGRIS center, Bureau of Library Kasetsart University. In addition, a set of inference rules is created to support the expertise retrieval task. By using AGRIX to implement an agricultural expertise retrieval, users can search for experts in two perspectives, plant (e.g., rice, sugar canes) and problem solving (e.g., plant diseases, fertilizers).