@article{zhang2006,
        title = {{Mining search engine query logs for query recommendation}},
        author = {Z. Zhang and O. Nasraoui},
        journal = {Proceedings of the 15th international conference on World Wide Web},
        pages = {1039--1040},
        publisher = {ACM Press New York, NY, USA},
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2f4873abd71cd109213b349c554cb376d/wnpxrz},
	keywords = {ir log query recommendation recommendersystems search}
}

@inproceedings{1260396,
        title = {Knowledge Discovery across Documents through Concept Chain Queries},
        address = {Washington, DC, USA},
        author = {Wei Jin and Rohini K. Srihari},
        booktitle = {ICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops},
        pages = {448--452},
        publisher = {IEEE Computer Society},
        year = 2006,
        url = {http://portal.acm.org/citation.cfm?id=1260396},
        isbn = {0-7695-2702-7},
	doi = {http://dx.doi.org/10.1109/ICDMW.2006.105},
	description = {Knowledge Discovery across Documents through Concept Chain Queries},
	abstract = {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.},
	biburl = {http://www.bibsonomy.org/bibtex/20342ecf31eb2c143344d6ecc213b507f/wnpxrz},
	keywords = {concept discovery document imported knowledge query}
}

@inproceedings{1183747,
        title = {Information retrieval from relational databases using semantic queries},
        address = {New York, NY, USA},
        author = {Anand Ranganathan and Zhen Liu},
        booktitle = {CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge management},
        pages = {820--821},
        publisher = {ACM},
        year = 2006,
        url = {http://portal.acm.org/citation.cfm?id=1183747},
        location = {Arlington, Virginia, USA},
	isbn = {1-59593-433-2},
	doi = {http://doi.acm.org/10.1145/1183614.1183747},
	description = {Information retrieval from relational databases using semantic queries},
	abstract = {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.},
	biburl = {http://www.bibsonomy.org/bibtex/2fb055e8b44a99ce23b3d491556c2ec72/wnpxrz},
	keywords = {imported ir query semantic}
}

@article{goas2003querying,
        title = {Querying Distributed Data through Distributed Ontologies: A Simple but Scalable Approach.},
        author = {François Goasdoué and Marie-Christine Rousset},
        journal = {IEEE Intelligent Systems},
        number = 5,
        pages = {60-65},
        volume = 18,
        year = 2003,
        url = {http://dblp.uni-trier.de/db/journals/expert/expert18.html#GoasdoueR03},
        ee = {http://csdl.computer.org/comp/mags/ex/2003/05/x5060abs.htm},
	date = {2003-11-27},
	description = {dblp},
	biburl = {http://www.bibsonomy.org/bibtex/2bd9eedf2210959679041f332901d6dd8/wnpxrz},
	keywords = {ontology p2p query}
}

@article{whittle2006,
        title = {{Query transformations and their role in Web searching by the general public}},
        author = {M. Whittle and B. Eaglestone and N. Ford and V.J. Gillet and A. Madden},
        journal = {Information Research},
        number = 1,
        volume = 12,
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2a3a38920006837a63ccfdcd7eda4d3fe/wnpxrz},
	keywords = {ir query search web}
}

@inproceedings{1220696,
        title = {Query expansion with the minimum user feedback by transductive learning},
        address = {Morristown, NJ, USA},
        author = {Masayuki Okabe and Kyoji Umemura and Seiji Yamada},
        booktitle = {HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing},
        pages = {963--970},
        publisher = {Association for Computational Linguistics},
        year = 2005,
        url = {http://portal.acm.org/citation.cfm?id=1220696},
        location = {Vancouver, British Columbia, Canada},
	doi = {http://dx.doi.org/10.3115/1220575.1220696},
	description = {Query expansion with the minimum user feedback by transductive learning},
	abstract = {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.},
	biburl = {http://www.bibsonomy.org/bibtex/235594a6522c6cb5354be87d05e93018a/wnpxrz},
	keywords = {expansion imported query}
}

@article{Tatti2006Computational,
        title = {Computational complexity of queries based on itemsets.},
        author = {Nikolaj Tatti},
        journal = {Inf. Process. Lett.},
        number = 5,
        pages = {183-187},
        volume = 98,
        year = 2006,
        url = {http://dblp.uni-trier.de/db/journals/ipl/ipl98.html#Tatti06},
        ee = {http://dx.doi.org/10.1016/j.ipl.2006.02.003},
	date = {2007-02-15},
	description = {dblp},
	biburl = {http://www.bibsonomy.org/bibtex/20f619c954398ebc28a27f3cd7dae43c6/wnpxrz},
	keywords = {complexity data paper proj:bk proj:et query toread}
}