Domain semantic mapping of database metasearch engine

In order to implement semantic mapping of database metasearch engines, a system is proposed, which uses ontology as the organization form of information and records the new words not appearing in the ontology. When the new word＇ s frequency of use exceeds the threshold, it is added into the ontology. Ontology expansion is implemented in this way. The search process supports ＂and＂ and ＂or＂ Boolean operations accordingly. In order to improve the mapping speed of the system, a memory module is added which can memorize the recent query information of users and automatically learn the user＇ s query interest during the mapping which can dynamically decide the search order of instances tables. Experiments prove that these measures can obviously reduce the average mapping time.

Aggregating metasearch engine results based on maximal entropy OWA operator

The maximal entropy ordered weighted averaging （ME-OWA） operator is used to aggregate metasearch engine results, and its newly analytical solution is also applied. Within the current context of the OWA operator, the methods for aggregating metasearch engine results are divided into two kinds. One has a unique solution, and the other has multiple solutions. The proposed method not only has crisp weights, but also provides multiple aggregation results for decision makers to choose from. In order to prove the application of the ME-OWA operator method, under the context of aggregating metasearch engine results, an example is given, which shows the results obtained by the ME-OWA operator method and the minimax linear programming （ minimax-LP ） method. Comparison between these two methods are also made. The results show that the ME-OWA operator has nearly the same aggregation results as those of the minimax-LP method.

Artificial neural network-based merging score for Meta search engine

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.