Web IR presents a new challenge due to the heterogeneity,the dynamic characteristic and the size of theWeb. A practical IR system that can satisfy the users' demand is very important ,in this paper we research the...Web IR presents a new challenge due to the heterogeneity,the dynamic characteristic and the size of theWeb. A practical IR system that can satisfy the users' demand is very important ,in this paper we research the charac-teristics of Web IR in detail and give out the ideal Web IR service model:it should include search engine spectrum,search engine hierarchy,search engine cooperative network. We also analyze the key technique of this model,propose asimple way for the Web IR service to deal with the huge-scale of Web resources easily,and test part of the ideas in ourprototype system SAInSE.展开更多
Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and i...Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.展开更多
文摘Web IR presents a new challenge due to the heterogeneity,the dynamic characteristic and the size of theWeb. A practical IR system that can satisfy the users' demand is very important ,in this paper we research the charac-teristics of Web IR in detail and give out the ideal Web IR service model:it should include search engine spectrum,search engine hierarchy,search engine cooperative network. We also analyze the key technique of this model,propose asimple way for the Web IR service to deal with the huge-scale of Web resources easily,and test part of the ideas in ourprototype system SAInSE.
文摘Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.
