Quality Estimation of Local Contents Based on PageRank Values of Web Pages

Abstract

Recently, it is getting more frequent to search not Web contents but local contents, e.g., by Google Desktop Search. Google succeeded in the Web search because of its PageRank algorithm for the ranking of the search results. PageRank estimates the quality of Web pages based on their popularity, which in turn is estimated by the number and the quality of pages referring to them through hyperlinks. This algorithm, however, is not applicable when we search local contents without link structure, such as text data. In this research, we propose a method to estimate the quality of local contents without link structure by using the PageRank values of Web contents similar to them. Based on this estimation, we can rank the desktop search results. Furthermore, this method enables us to search contents across different resources such as Web contents and local contents. In this paper, we applied this method to Web contents, calculated the scores that estimate their quality, and we compare them with their page quality scores by PageRank.