In the World Wide Web there are innumerable information sources containing very useful information that cannot be indexed by general-purpose search engines and hence cannot be visited by most common users. Of course, users can search a source through its query interface if they know where the source can be found. The idea of querying and collating results from multiple databases is not new. Internet meta-search engines, online catalogues, multi-databases and other kinds of information integration systems have attracted a lot of attention since the advent of the network. In the web's early days, it used to be that a search engine either presented crawler-based results or human-powered listings. Today, it is extremely common for both types of results to be presented. Usually, a hybrid search engine will favor one type of listings over another. Often the user is interested in items that are both visually and semantically similar. With a view to supporting such functionality, the hybrid search engine provides a novel retrieval method, in which both visual and ontology search is employed for the same query. This novel method automatically combines different types of search results, and complements content-based search with ontology-based search and vice versa. In this paper, we study the rank aggregation problem in the context of the web, i. e. the problem of ranking result from various sources. There are various ranking aggregation methods available. We design an algorithm, based on which we propose a new rank aggregation method. It is observed that our proposed method is more effective and efficient than other well-known methods.

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Crawling Multi-criteria Selection Meta Search Engines Rank Aggregation And Word Association