BibSonomy publications for /user/jaeschke/engineThu Jan 27 12:08:28 CET 2011New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082008 engine information l3s logsonomy myown retrieval search wp5 analysis network sna social Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.Sat Mar 27 18:34:13 CET 2010Menlo Park, CA, USAProceedings of the Second International Conference on Weblogs and Social Media (ICWSM 2008)192--193Logsonomy -- A Search Engine Folksonomy20082008 engine folksonomy l3s logsonomy myown search wp5 In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us. Fri Oct 16 10:26:15 CEST 2009New York, NY, USAWSDM (Late Breaking-Results)conf/wsdm/2009Tagging with Queries: How and Why?2009engine logsonomy query-log-analysis search tagging Web search queries capture the information need of search engine users. Search engines store these queries in their logs and analyze them to guide their search results. In this work, we argue that not only a search engine can benefit from data stored in these logs, but also the web users. We first show how clickthrough logs can be collected in a distributed fashion using the http referer field in web server access logs. We then perform a set of experiments to study the information value of search engine queries when treated as "tags" or "labels" for the web pages that both appear as a result and the user actually clicks on. We ask how much extra information these query tags provide for web pages by comparing them to tags from the del.icio.us bookmarking site and to the pagetext. We find that query tags can provide substantially many (on average 250 tags per URL), new tags (on average 125 tags per URL are not present in the pagetext) for a large fraction of the Web.Wed Jun 10 10:08:20 CEST 2009Computer Networksjun3303--310A novel Web usage mining approach for search engines392002engine mining search usage web Web usage mining can be very useful to search engines. This paper proposes a novel effective approach to exploit the relationships among users, queries and resources based on the search engine's log. How this method can be applied is illustrated by a Chinese image search engine.Tue May 16 12:12:26 CEST 2006New York, NY, USAProceedings of the 13th international conference on World Wide Web413--421A community-aware search engine2004search engine detection hits community network Current search technologies work in a "one size fits all" fashion. Therefore, the answer to a query is independent of specific user information need. In this paper we describe a novel ranking technique for personalized search servicesthat combines content-based and community-based evidences. The community-based information is used in order to provide context for queries andis influenced by the current interaction of the user with the service. Ouralgorithm is evaluated using data derived from an actual service available on the Web an online bookstore. We show that the quality of content-based ranking strategies can be improved by the use of communityinformation as another evidential source of relevance. In our experiments the improvements reach up to 48% in terms of average precision.Fri Mar 31 11:28:50 CEST 2006CIKM652-659Swoogle: a search and metadata engine for the semantic web.2004search web engine metadate swoogle semantic seminar2006