E-commerce Website Ranking Using Semantic Web Mining and Neural Computing

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.

Artificial neural network-based merging score for Meta search engine

In a short span of time, less than 15 years, the web search process is modified enormously because of magnificent growth in web based information resources. The speedy expansion of web is enjoyable because of the increase in information resources but at the same time its huge size and interference of SEOs in search process lead to increased difficulty in extracting relevant information from the web. Personalized web search may be the solution to relevancy problem but user is reluctant in giving his personal information because of privacy concerns [1]. Moreover most existing web mining algorithms do not possess attractive time and space complexities and hence lead to sufferings of novice user. This paper addresses above mentioned issues of Search Engine domain and intends to implement intelligent web mining in the form of Web Page Ranking Tool so as to improve the web page ranking process through incorporation of Back Propagation neural networks. [2][3][4].

http://ieeexplore.ieee.org/iel7/6937353/6949036/06949254.pdf

Intelligent web mining to ameliorate Web Page Rank using Back- Propagation neural network

Thinking of todays web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web. Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages. On other hand, semantic search engines provide efficient and relevant results as the semantic web manages information with well defined meaning using ontology. A Meta-Search engine is a search tool that forwards users query to several existing search engines and provides combined results by using their own page ranking algorithm. SemanTelli is a meta semantic search engine that fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot through intelligent agents. This paper proposes enhancement of SemanTelli with improved snippet analysis based page ranking algorithm and support for image and news search.

Experience of Developing a Meta-Semantic Search Engine

Thinking of today's web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web. Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages. On other hand, semantic search engines provide efficient and relevant results as the semantic web manages information with well defined meaning using ontology. A Meta-Search engine is a search tool that forwards users query to several existing search engines and provides combined results by using their own page ranking algorithm. SemanTelli is a meta-semantic search engine that fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot through intelligent agents. This paper proposes enhancement of SemanTelli with improved snippet analysis based page ranking algorithm and support for image and news search.

Experience of Developing a Meta-semantic Search Engine

Considering today's web scenario, there is a need of effective and meaningful search over the web which is provided by Semantic Web. Existing search engines are keyword based. They are vulnerable in answering intelligent queries from the user due to the dependence of their results on information available in web pages. While semantic search engines provides efficient and relevant results as the semantic web is an extension of the current web in which information is given well defined meaning. MetaCrawler is a search tool that uses several existing search engines and provides combined results by using their own page ranking algorithm. This paper proposes development of a meta-semantic-search engine called SemanTelli which works within cloud. SemanTelli fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot with the help of intelligent agents that eliminate the limitations of existing search engines.

Trupti Pagare

Papers

Experience of Developing a Meta-Semantic Search Engine

Experience of Developing a Meta-semantic Search Engine

Intelligent Agent Based Semantic Web in Cloud Computing Environment