Abstract:

In order to improve the precision of meta search engine, an improved merging algorithm of meta search results is proposed. In this algorithm, first, the text-based information obtained from search results is analyzed and both the query-match grade and the result relevancy are considered to give an approach on the text normalization for meta search. Next, the relevant scores of documents are normalized by incorporating text analysis with the ranks given by the search engines for the purpose of adjusting the local similarities. Then, based on the performance evaluation of underlying search engines, an improved shadow document method is presented to evaluate the scores of non-relevant documents. Finally, a merging method based on the group decision making is adopted to sort the search results. It is found from the tested results in an actual Web environment that the search results obtained by the proposed algorithm are of higher relativity than those by the existing merging algorithms.

Key words: information retrieval, meta search, search-resuh merging, text analysis, group decision making