Artificial neural network-based merging score for Meta search engine
| Autor: | Santosh Kumar Ray, P. Vijaya, G. Raju |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Artificial neural network Exploit Computer science Computation InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Search aggregator Metals and Alloys General Engineering Response time 02 engineering and technology Snippet computer.software_genre Search engine 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Metasearch engine computer |
| Zdroj: | Journal of Central South University. 23:2604-2615 |
| ISSN: | 2227-5223 2095-2899 |
| DOI: | 10.1007/s11771-016-3322-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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