Complex Adaptive Agriculture Vertical Search Model And Its Implementation

By the end of 2009, there have been more than 30000 agricultural web sites on the internet, which cover almost all kinds of agricultural information, such as agricultural technology, market information, agricultural news and policies. However, agricultural information on the web has no uniform representation and is heterogeneous, distributed and redundant, which forms isolated information islands. Since the knowledge of farmers to operate a computer is limited, it would be hard for them to use traditional search tools to acquire and filter personalized information on the web. Facing huge amount of information, farmers are often frustrated and the phenomenon of "information overload" is a serious matter here. Obviously, it is significant to develop personalized, intelligent and professional web search models and tools.For the characteristics of openness, scatterings, hierarchy, evolution and hugeness of internet, an agricultural search model based on complex adaptive system is proposed in this dissertation. This model constructs the agent alliance of agricultural information discovery agent, information acquisition agent, information processing agent and service agent. The model fit the complex and dynamic internet environment through learning mechanisms between agents and web contents, representation methods and user needs. The method proposed improves the precision and recall of agricultural search engine and solves the core problem for the next generation search engine.For the characteristics of dynamics and high scattering of web resources, AADWED (Adaptive Agriculture Deep Web Entry Discovery) algorithm is proposed to acquire domain-specific deep web resources effectively and efficiently. This algorithm constantly constructs queries according to the sample and submits the queries to a search engine in order to find the entry page of hidden web resources. The experiments validate that this method can significantly improve the efficiency of finding hidden web resources.Aiming at the two characteristics (dynamics and diversity) of web pages on the web sites, an adaptive web structural data extraction algorithm is presented in this dissertation. This algorithm is based on traditional MDR algorithm and adopts relative entropy theory for noise removal so as to improve the precision of web structural data extraction. Aiming at huge amount of heterogeneous, incomplete and redundant agricultural information on the web, this dissertation studied the automatic spatial property annotation and processing redundant data based on semantics for agricultural product price and buy/sell information. The proposed method improves the quality of data and constructs a fundamental for precise retrieval and visualization.To tackle the problem of personalized information needs from different web users, a new approach that automatically mining web user profile based on FCA is proposed. The interest models of web users are represented as formal concepts and the relationship between these models are described in a concept lattice. The method of assessing document relevance to the topics is also proposed. The experiments show that our approach is effective.At last, based on the complex adaptive agricultural search model proposed in this dissertation, agricultural vertical search engine "Sounong" has been designed and implemented. This search engine has served publicly for many provinces.