Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Abstract. In this paper we present static and dynamic studies of duplicate and near-duplicate documents in the Web. The static and dynamic studies involve the analysis of similar content among pages within a given snapshot of the Web and how pages in an old snapshot are reused to compose new documents in a more recent snapshot. We ran a series of experiments using four snapshots of the Chilean Web. In the static study, we identify duplicates in both parts of the Web graph – reachable (connected by links) and unreachable components (unconnected) – aiming to identify where duplicates occur more frequently. We show that the number of duplicates in the Web seems to be much higher than previously reported (about 50 % higher) and in our data the duplicated in the unreachable Web is 74,6 % higher than the number of duplicates in the reachable component of the Web graph. In the dynamic study, we show that some of the old content is used to compose new pages. If a page in a newer snapshot has content of a page in an older snapshot, we say that the source is a parent of the new page. We state the hypothesis that people use search engines to find pages and republish their content as a new document. We present evidences that this happens for part of the pages that have parents. In this case, part of the Web content is biased by the ranking function of search engines. 1

Abstract. This document summarizes the results of an experiment made in the