BibSonomy publications for /user/jaeschke/searchFri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008search logsonomy for:nepomuk engine retrieval analysis network l3s information wp5 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.Fri Jun 20 14:39:26 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy2008folksonomy myown 2008 logsonomy for:nepomuk search l3s engine 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.Wed Feb 13 10:49:41 CET 2008Web Intelligence, IEEE/WIC/ACM International Conference on423-426Using PersonalizedWeb Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systems2007web search folksonomy A large part of the modern web is characterized by usergenerated content categorized using collaborative tagging or folksonomy. It becomes difficult to search for relevant content because of ambiguity in lexical representation of concepts and variances in preferences of users. With more and more services relying on tags for content categorization, it is important that search techniques evolve to better suit the scenario. A promising approach towards solving these problems is to use machine common sense in conjunction with folksonomy. A past attempt to use this approach has shown positive results in finding relevant content but it does not address the issue of noise in search results. In this paper, we use the personalized web search technique of traditional web search systems to address the issue of irrelevant search results in common sense and folksonomy based search systems. In personalized web search, results are reflective of user's preferences, which are decided by search history and categories of interest. We propose modifications to personalized web search technique. Using this modified approach, we extend the basic common sense and folksonomy based search systems to address the issue of noise in search results.Thu Feb 01 14:04:37 CET 2007HeidelbergThe Semantic Web: Research and ApplicationsJune411-426Lecture Notes in Computer ScienceInformation Retrieval in Folksonomies: Search and Ranking40112006l3s pagerank 2006 rank information myown graph retrieval search folksonomy folkrank iccs_example seminar2006 trias_example mining ranking Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.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 network engine community hits detection 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 Apr 21 12:37:28 CEST 2006Computer Networks and ISDN Systems1--7107--117The anatomy of a large-scale hypertextual Web search engine301998ranking web search pagerank Thu Apr 13 11:41:37 CEST 2006Pacific Grove, California, United StatesProceedings of the 13th ACM Symposium on Operating Systems Principles516-25Semantic File Systems.251991file system semantic search tagging A semantic file system is an information storage system that provides flexible associative access to the system's contents by automatically extracting attributes from files with file type specific transducers. Associative access is provided by a conservative extension to existing tree-structured file system protocols, and by protocols that are designed specifically for content based access. Compatiblity with existing file system protocols is provided by introducing the concept of a virtual directory. Virtual directory names are interpreted as queries, and thus provide flexible associative access to files and directories in a manner compatible with existing software. Rapid attribute-based access to file system contents is implemented by automatic extraction and indexing of key properties of file system objects. The automatic indexing of files and directories is called "semantic" because user programmable transducers use information about the semantics of updated file system objects to extract the properties for indexing. Experimental results from a semantic file system implementation support the thesis that semantic file systems present a more effective storage abstraction than do traditional tree structured file systems for information sharing and command level programming.Tue Apr 11 18:13:27 CEST 2006Galway, IrelandProceedings of the 1st Workshop on The Semantic Desktop at the ISWC 2005 ConferenceNovember92 - 106Semantically Enhanced Searching and Ranking on the Desktop2005semantic beagle desktop ranking seminar2006 search Tue Apr 04 12:12:47 CEST 2006Searching in a small worldlicentiate thesis2005search small world peer p2p searching Fri Mar 31 11:28:50 CEST 2006CIKM652-659Swoogle: a search and metadata engine for the semantic web.2004seminar2006 metadate web search for:kde swoogle engine semantic Wed Jan 11 11:45:07 CET 2006TorontoProceedings of the Eighth International World Wide Web Conference WWW-1999MayFinding related pages in the World Wide Web1999related search web