The problem of associating the agricultural market names on web sites with their locations is essential for geographical analysis of the agricultural products. In this paper, an algorithm which employs the administrative ontology and the statistics from the search results were proposed. The experiments with 100 market names collected from web sites were conducted. The experimental results demonstrate that the algorithm proposed obtains satisfactory performance in resolving the problem above, thus the effectiveness of the method is verified.

More web pages are widely applying AJAX (Asynchronous JavaScript XML) due to the rich interactivity and incremental communication. By observing, it is found that the AJAX contents, which could not be seen by traditional crawler, are well-structured and belong to one specific domain generally. Extracting the structured data from AJAX contents and annotating its semantic are very significant for further applications. In this paper, a structured AJAX data extraction method for agricultural domain based on agricultural ontology was proposed. Firstly, Crawljax, an open AJAX crawling tool, was overridden to explore and retrieve the AJAX contents; secondly, the retrieved contents were partitioned into items and then classified by combining with agricultural ontology. HTML tags and punctuations were used to segment the retrieved contents into entity items. Finally, the entity items were clustered and the semantic annotation was assigned to clustering results according to agricultural ontology. By experimental evaluation, the proposed approach was proved effectively in resource exploring, entity extraction, and semantic annotation.

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.

Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks: the curse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.