RicardoBaeza-Yates Graphs from Search Engine Queries SOFSEM 2007: Theory and Practice of Computer Science 1–8 2007 Server logs of search engines store traces of queries submitted by users, which include queries themselves along with Web pages selected in their answers. Here we describe several graph-based relations among queries and many applications where these graphs could be used. RicardoBaeza-Yates AlessandroTiberi Extracting semantic relations from query logs ACM 76–85 2007 MarkoGrobelnik DunjaMladeni\'c Efficient text categorization 1998 We present an approach to text categorization using machine learning techniques. The approach is developed and tested on large text hierarchy named Yahoo that is available on the Web. We handle the large number of features and training examples by taking into account hierarchical structure of examples and using feature subset selection for large text data. The large number of categories is handled separately for each testing example by pruning unpromising categories. In this way, the number of... RaviKumar JasmineNovak AndrewTomkins Structure and evolution of online social networks ACM 611–617 2006 In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network. B\"orkurSigurbj\"ornsson RoelofvanZwol Flickr tag recommendation based on collective knowledge ACM 327–336 2008 BörkurSigurbjörnsson RoelofvanZwol Flickr tag recommendation based on collective knowledge ACM 327–336 2008 B�rkurSigurbj�rnsson RoelofvanZwol Flickr tag recommendation based on collective knowledge ACM 327–336 2008 DannySullivan Yahoo Renews With Google, Changes Results 2002 HaraldWeiss Yahoo ist zu allem bereit – aber nur wenn Microsoft mehr bezahlt Computer Zeitung 16 2008 ZhichenXu YunFu JianchangMao DifuSu Towards the Semantic Web: Collaborative Tag Suggestions 2006 Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.