Finding Potential Seeds through Rank Aggregation of Web Searches

This paper presents a potential seed selection algorithm for web crawlers using a gain - share scoring approach. Initially we consider a set of arbitrarily chosen tourism queries. Each query is given to the selected N commercial Search Engines (SEs); top m search results for each SE are obtained, and each of these m results is manually evaluated and assigned a relevance score. For each of m results, a gain - share score is computed using their hyperlinks structure across N ranked lists. Gain score of each link present in each of m results and a portion of the gain score is propagated to the share score of each of m results. This updated share scores of each of m results determine the potential set of seed URLs for web crawling. Experimental results on tourism related web data illustrate the effectiveness of the proposed seed selection algorithm.

• Type: Journal
• Year: 2011
• Where: PREMI
• Authors: Rajendra Prasath, Pinar Öztürk
• Keywords: Commercial Search Engines, Pattern Recognition, PREMI 2011, Relevance Score, Selection Algorithm