http://www.bibsonomy.org/burst/user/wnpxrz/queryBibSonomy BuRST Feed for /user/wnpxrz/query2008-07-21T01:53:26+02:00http://www.bibsonomy.org/bibtex/2f4873abd71cd109213b349c554cb376d/wnpxrzwnpxrz2008-04-07T16:20:08+02:00recommendersystems log recommendation query ir search <span style="color:#555555;">Z. <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and O. <a href="http://www.bibsonomy.org/author/Nasraoui">Nasraoui</a> </span><em>Proceedings of the 15th international conference on World Wide Web</em>(<em>2006</em>)Mon Apr 07 16:20:08 CEST 2008Proceedings of the 15th international conference on World Wide Web1039--1040{Mining search engine query logs for query recommendation}2006recommendersystems log recommendation query ir search Knowledge Discovery across Documents through Concept Chain Querieshttp://www.bibsonomy.org/bibtex/20342ecf31eb2c143344d6ecc213b507f/wnpxrzwnpxrz2007-12-03T17:39:23+01:00document query knowledge concept imported discovery <span style="color:#555555;">Wei <a href="http://www.bibsonomy.org/author/Jin">Jin</a> and Rohini K. <a href="http://www.bibsonomy.org/author/Srihari">Srihari</a> </span><em>ICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops, </em><em>page448--452. </em><em>Washington, DC, USA, </em><em>IEEE Computer Society, </em>(<em>2006</em>)Mon Dec 03 17:39:23 CET 2007Washington, DC, USAICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops448--452Knowledge Discovery across Documents through Concept Chain Queries2006document query knowledge concept imported discovery This paper focuses on detecting links between two concepts across text documents (e.g. two persons). We interpret such a query as finding the most meaningful evidence trail across documents that connect these two concepts. Here we propose a fast and efficient algorithm to perform this task. It is based on the idea of hypothesis generation originated by Swanson called "complementary structures in disjoint literatures" (CSD). We adapted the technique by (i) developing an alternate method of generating semantic profiles and (ii) extending the technique to generate concept chains. Counterterrorism corpus is used to evaluate the performance of this approach and demonstrates the effectiveness of our algorithm.Information retrieval from relational databases using semantic querieshttp://www.bibsonomy.org/bibtex/2fb055e8b44a99ce23b3d491556c2ec72/wnpxrzwnpxrz2007-11-30T20:33:26+01:00query semantic imported ir <span style="color:#555555;">Anand <a href="http://www.bibsonomy.org/author/Ranganathan">Ranganathan</a> and Zhen <a href="http://www.bibsonomy.org/author/Liu">Liu</a> </span><em>CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management, </em><em>page820--821. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Fri Nov 30 20:33:26 CET 2007New York, NY, USACIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management820--821Information retrieval from relational databases using semantic queries2006query semantic imported ir Relational databases are widely used today as a mechanism for providing access to structured data. They, however, are not suitable for typical information finding tasks of end users. There is often a semantic gap between the queries users want to express and the queries that can be answered by the database. In this paper, we propose a system that bridges this semantic gap using domain knowledge contained in ontologies. Our system extends relational databases with the ability to answer semantic queries that are represented in SPARQL, an emerging Semantic Web query language. Users express their queries in SPARQL, based on a semantic model of the data, and they get back semantically relevant results. We define different categories of results that are semantically relevant to the users' query and show how our system retrieves these results. We evaluate the performance of our system on sample relational databases, using a combination of standard and custom ontologies.dblphttp://www.bibsonomy.org/bibtex/2bd9eedf2210959679041f332901d6dd8/wnpxrzwnpxrz2007-11-19T16:10:02+01:00p2p ontology query <span style="color:#555555;">Fran&#231;ois <a href="http://www.bibsonomy.org/author/Goasdoué">Goasdou&#233;</a> and Marie-Christine <a href="http://www.bibsonomy.org/author/Rousset">Rousset</a> </span><em>IEEE Intelligent Systems</em><em>18(5):60-65</em>(<em>2003</em>)Mon Nov 19 16:10:02 CET 2007IEEE Intelligent Systems560-65Querying Distributed Data through Distributed Ontologies: A Simple but Scalable Approach.182003p2p ontology query http://www.bibsonomy.org/bibtex/2a3a38920006837a63ccfdcd7eda4d3fe/wnpxrzwnpxrz2007-08-27T18:29:23+02:00search query web ir <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Whittle">Whittle</a> and B. <a href="http://www.bibsonomy.org/author/Eaglestone">Eaglestone</a> and N. <a href="http://www.bibsonomy.org/author/Ford">Ford</a> and V.J. <a href="http://www.bibsonomy.org/author/Gillet">Gillet</a> and A. <a href="http://www.bibsonomy.org/author/Madden">Madden</a> </span><em>Information Research</em>(<em>2006</em>)Mon Aug 27 18:29:23 CEST 2007Information Research1{Query transformations and their role in Web searching by the general public}122006search query web ir Query expansion with the minimum user feedback by transductive learninghttp://www.bibsonomy.org/bibtex/235594a6522c6cb5354be87d05e93018a/wnpxrzwnpxrz2007-08-06T17:19:17+02:00expansion imported query <span style="color:#555555;">Masayuki <a href="http://www.bibsonomy.org/author/Okabe">Okabe</a> and Kyoji <a href="http://www.bibsonomy.org/author/Umemura">Umemura</a> and Seiji <a href="http://www.bibsonomy.org/author/Yamada">Yamada</a> </span><em>HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, </em><em>page963--970. </em><em>Morristown, NJ, USA, </em><em>Association for Computational Linguistics, </em>(<em>2005</em>)Mon Aug 06 17:19:17 CEST 2007Morristown, NJ, USAHLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing963--970Query expansion with the minimum user feedback by transductive learning2005expansion imported query Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user's manual judgment of those documents would much help to select good expansion terms, it is difficult to get enough feedback from users in practical situations. In this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document. In order to tackle this specific condition, we introduce two refinements to a well-known query expansion technique. One is application of a transductive learning technique in order to increase relevant documents. The other is a modified parameter estimation method which laps the predictions by multiple learning trials and try to differentiate the importance of candidate terms for expansion in relevant documents. Experimental results show that our technique outperforms some traditional query expansion methods in several evaluation measures.dblphttp://www.bibsonomy.org/bibtex/20f619c954398ebc28a27f3cd7dae43c6/wnpxrzwnpxrz2007-06-28T12:35:02+02:00proj:bk data paper toread query proj:et complexity <span style="color:#555555;">Nikolaj <a href="http://www.bibsonomy.org/author/Tatti">Tatti</a> </span><em>Inf. Process. Lett.</em><em>98(5):183-187</em>(<em>2006</em>)Thu Jun 28 12:35:02 CEST 2007Inf. Process. Lett.5183-187Computational complexity of queries based on itemsets.982006proj:bk data paper toread query proj:et complexity