http://www.bibsonomy.org/burst/user/brightbyte/information-retrievalBibSonomy BuRST Feed for /user/brightbyte/information-retrieval2008-08-21T05:03:01+02:00http://www.bibsonomy.org/bibtex/2e157ee6010babd7cfd826aee0e25914d/brightbytebrightbyte2008-05-22T23:40:58+02:00ontology vocabulary information-retrieval knowledge SKOS <span style="color:#555555;">Alistair <a href="http://www.bibsonomy.org/author/Miles">Miles</a> </span>(<em>2006</em>)Thu May 22 23:40:58 CEST 2008A Theory of Retrieval Using Structured Vocabularies2006ontology vocabulary information-retrieval knowledge SKOS A primary motivation for the development of the Semantic Web has been the need for effective information retrieval systems which may be realised through vocabulary control and the use of structured metadata. The technological framework of the Web (URI, HTTP, XML) and of the Semantic Web (RDF, OWL, SPARQL) provides a platform upon which distributed data and metadata applications may be constructed, but does not in itself provide any direct support for information retrieval applications per se. Widely applicable Semantic Web languages that extend this basic layer and provide generic support for retrieval applications, in addition to good practice guidelines and design patterns for developing such applications, are required. The ultimate purpose of this report is to develop a formal theory of retrieval using controlled vocabularies that have a simple and intuitive structure, to provide the necessary theoretical foundations for the development of Semantic Web languages and design patterns for distributed retrieval applications. The main body of this report is devoted to the articulation of such a theory. The theory is expressed formally through the use of mathematical notation, with the intention that this level of formality will provide the bridge between informal requirements specifications and the implementation of effective retrieval applications in computer systems. Specifically, a theory is developed to describe the ways in which a structured vocabulary may be used to construct an index over a collection of objects and then used to express queries which may be evaluated against an index to obtain a set of results. This theory is extended to consider ways in which both the precision and recall of retrieval strategies may be improved, through the use of expansion and ranking techniques and through “coordination”. The problem of translating between controlled vocabularies is also considered. The theory attempts to formalise, unify and extend the traditional wisdom of the library sciences regarding the use of thesauri, classification schemes, subject heading systems, taxonomies and other types of structured vocabulary, so that proven techniques and methodologies may be transferred to a Semantic Web context. The recently chartered W3C Semantic Web Deployment Working Group has been charged with the development of the Simple Knowledge Organisation System (SKOS) to W3C Recommendation status. SKOS is a Semantic Web language specifically intended to support information retrieval applications using controlled vocabularies that have a relatively simple structure. A formal requirements specification is the first planned deliverable in the standardisation of SKOS. An immediate goal of this report is to provide a level of abstraction that can be used to perform a comparative analysis of use cases involving information retrieval systems that operate with structured vocabularies, so that the requirements of these systems with respect to Semantic Web languages such as SKOS may be clearly determined. Also, this report suggests ways in which the theory may be mapped to concrete language constructs and representation patterns in Semantic Web languages. In so doing it is hoped that the development of SKOS and similar languages may be grounded with sufficient rigour to ensure their wide applicability and consistent use.http://www.bibsonomy.org/bibtex/2f36a2d208aa4bf661ac0a826f271762f/brightbytebrightbyte2008-05-17T00:02:56+02:00search information-retrieval thesaurus <span style="color:#555555;">Wolfgang G. <a href="http://www.bibsonomy.org/author/Stock">Stock</a> </span><em>Oldenbourg, </em>(<em>2007</em>)Sat May 17 00:02:56 CEST 2008Information Retrieval2007search information-retrieval thesaurus http://www.bibsonomy.org/bibtex/262e39eef0b469b4a7058b02dfa1ca5fb/brightbytebrightbyte2008-05-14T01:06:57+02:00WW-CITED semantic-gap WW-MUST semantic information-retrieval indexing <span style="color:#555555;">H. <a href="http://www.bibsonomy.org/author/Davulcu">Davulcu</a> and H.V. <a href="http://www.bibsonomy.org/author/Nguyen">Nguyen</a> and V. <a href="http://www.bibsonomy.org/author/Ramachandran">Ramachandran</a> </span><em>International Journal of Intelligent Information Technologies</em><em>2(3):1--20</em>(<em>2006</em>)Wed May 14 01:06:57 CEST 2008International Journal of Intelligent Information Technologies31--20Boosting Item Findability:Bridging the Semantic Gap Between Search Phrases and Item Information22006WW-CITED semantic-gap WW-MUST semantic information-retrieval indexing http://www.bibsonomy.org/bibtex/27e0304a4098c440b5606539b79acbb8a/brightbytebrightbyte2008-03-24T14:58:38+01:00classic WW-SHOULD indexing WW-CITED clustering information-retrieval semantic <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Deerwester">Deerwester</a> and S.T. <a href="http://www.bibsonomy.org/author/Dumais">Dumais</a> and G.W. <a href="http://www.bibsonomy.org/author/Furnas">Furnas</a> and T.K. <a href="http://www.bibsonomy.org/author/Landauer">Landauer</a> and R. <a href="http://www.bibsonomy.org/author/Harshman">Harshman</a> </span><em>Journal of the American Society for Information Science</em><em>41(6):391--407</em>(<em>1990</em>)Mon Mar 24 14:58:38 CET 2008Journal of the American Society for Information Science6391--407{Indexing by latent semantic analysis}411990classic WW-SHOULD indexing WW-CITED clustering information-retrieval semantic http://www.bibsonomy.org/bibtex/29add0eb8e86982482c2156454dd294dc/brightbytebrightbyte2008-03-22T13:02:26+01:00information-retrieval WW-SHOULD thesaurus WW-LIB <span style="color:#555555;">H.P. <a href="http://www.bibsonomy.org/author/Luhn">Luhn</a> </span><em>Spartan Books, </em>(<em>1968</em>)Sat Mar 22 13:02:26 CET 2008{HP Luhn: Pioneer of Information Science: Selected Works}1968information-retrieval WW-SHOULD thesaurus WW-LIB http://www.bibsonomy.org/bibtex/26d10f5e6dbec745103a3de4b6d4d4165/brightbytebrightbyte2008-03-21T12:55:34+01:00semantic information-retrieval WW-CITED WW-MUST semantic-gap indexing <span style="color:#555555;">C.A. <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> and A. <a href="http://www.bibsonomy.org/author/Gupta">Gupta</a> and M. <a href="http://www.bibsonomy.org/author/Batool">Batool</a> and S. <a href="http://www.bibsonomy.org/author/Trehan">Trehan</a> </span><em>Journal of Computing and Information technology</em><em>14(3):191</em>(<em>2006</em>)Fri Mar 21 12:55:34 CET 2008Journal of Computing and Information technology3191{Latent Semantic Indexing-Based Intelligent Information Retrieval System for Digital Libraries}142006semantic information-retrieval WW-CITED WW-MUST semantic-gap indexing http://www.bibsonomy.org/bibtex/2071311be6948472355926d313d52186f/brightbytebrightbyte2008-03-21T12:31:17+01:00indexing semantic search information-retrieval <span style="color:#555555;">W.I. <a href="http://www.bibsonomy.org/author/Grosky">Grosky</a> and R. <a href="http://www.bibsonomy.org/author/Zhao">Zhao</a> </span><em>Pattern Recognition</em>(<em>2002</em>)Fri Mar 21 12:31:17 CET 2008Pattern Recognition593--600Negotiating the semantic gap: From feature maps to semantic landscapes32002indexing semantic search information-retrieval http://www.bibsonomy.org/bibtex/26325958691c2bd07df973789388c1d53/brightbytebrightbyte2008-02-26T17:45:02+01:00semantic query-expansion information-retrieval wikipedia search <span style="color:#555555;">Turdakov <a href="http://www.bibsonomy.org/author/Denis">Denis</a> </span>(<em>2007</em>)Tue Feb 26 17:45:02 CET 2008 Recommender System Based on User-generated Content 2007 semantic query-expansion information-retrieval wikipedia search http://www.bibsonomy.org/bibtex/2e11c412fbeb95303429892e447598aea/brightbytebrightbyte2008-02-26T12:01:49+01:00wikipedia text-mining information-retrieval web <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Gleim">Gleim</a> and A. <a href="http://www.bibsonomy.org/author/Mehler">Mehler</a> and M. <a href="http://www.bibsonomy.org/author/Dehmer">Dehmer</a> </span><em>Web as Corpus</em>(<em>2006</em>)Tue Feb 26 12:01:49 CET 2008Web as CorpusWeb Corpus Mining by instance of Wikipedia2006wikipedia text-mining information-retrieval web http://www.bibsonomy.org/bibtex/292b2425ab2c47445ad055f36970752b9/brightbytebrightbyte2008-02-26T11:57:49+01:00information-retrieval semantic-web skos <span style="color:#555555;">Alistair <a href="http://www.bibsonomy.org/author/Miles">Miles</a> </span><em>April2005. </em>Tue Feb 26 11:57:49 CET 2008AprilSKOS: Simple Knowledge Organisation for the Web2005information-retrieval semantic-web skos This article introduces the Simple Knowledge Organisation System (SKOS), a Semantic Web language for representing controlled structured vocabularies, including thesauri, classification schemes, subject heading systems, and taxonomies. SKOS provides a framework for publishing thesauri, classification schemes, and subject indexes on the Web, and for applying these systems to resource collections that are part of the Semantic Web. Semantic Web applications may harvest and merge SKOS data, to integrate and enhance retrieval service across multiple collections (e.g., libraries). This article also describes some alternatives for integrating Semantic Web services based on the Resource Description Framework (RDF) and SKOS into a distributed enterprise architecture. doi:10.1300/J104v43n03\_05http://www.bibsonomy.org/bibtex/2017de3ca82fbf15d92f5dbd18bdb4727/brightbytebrightbyte2008-02-26T11:54:51+01:00information-retrieval link-mining web <span style="color:#555555;">Allan <a href="http://www.bibsonomy.org/author/Borodin">Borodin</a> and Gareth O. <a href="http://www.bibsonomy.org/author/Roberts">Roberts</a> and Jeffrey S. <a href="http://www.bibsonomy.org/author/Rosenthal">Rosenthal</a> and Panayiotis <a href="http://www.bibsonomy.org/author/Tsaparas">Tsaparas</a> </span>(<em>2005</em>)Tue Feb 26 11:54:51 CET 2008Link Analysis Ranking Algorithms Theory And Experiments2005information-retrieval link-mining web The explosive growth and the widespread accessibility of the Web has led to surge of research activity in the area of information retrieval on the World Wide Web. The seminal papers of Kleinberg [31], and Brin and Page [9] introduced Link Analysis Ranking, where hyperlink structures are used to determine the relative authority of a Web page, and produce improved algorithms for the ranking of Web search results. In this paper we work within the hubs and authorities framework defined by...stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2676a8ca47f10b35263ce196e700a8d9e/brightbytebrightbyte2008-02-10T02:19:38+01:00information-retrieval link-mining web <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Chakrabarti">Chakrabarti</a> and B. E. <a href="http://www.bibsonomy.org/author/Dom">Dom</a> and S. R. <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> and P. <a href="http://www.bibsonomy.org/author/Raghavan">Raghavan</a> and S. <a href="http://www.bibsonomy.org/author/Rajagopalan">Rajagopalan</a> and A. <a href="http://www.bibsonomy.org/author/Tomkins">Tomkins</a> and D. <a href="http://www.bibsonomy.org/author/Gibson">Gibson</a> and J. <a href="http://www.bibsonomy.org/author/Kleinberg">Kleinberg</a> </span><em>Computer</em><em>32(8):60--67</em>(<em>1999</em>)Sun Feb 10 02:19:38 CET 2008Computer860--67Mining the Web's link structure321999information-retrieval link-mining web The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an \&ldquo;off-the-shelf\&rdquo; database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updatedstuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2ac02f1d7dea7a106bc4103c8a9ec4aef/brightbytebrightbyte2008-02-10T02:19:38+01:00web link-mining information-retrieval <span style="color:#555555;">Lise <a href="http://www.bibsonomy.org/author/Getoor">Getoor</a> and Christopher P. <a href="http://www.bibsonomy.org/author/Diehl">Diehl</a> </span><em>SIGKDD Explor. Newsl.</em><em>7(2):3--12</em><em>December2005. </em>Sun Feb 10 02:19:38 CET 2008New York, NY, USASIGKDD Explor. Newsl.December23--12Link mining: a survey72005web link-mining information-retrieval stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2d0dcb5fa75252409f762e84ce3c03a34/brightbytebrightbyte2008-02-10T02:19:38+01:00wikipedia information-retrieval link-mining <span style="color:#555555;">A. D. <a href="http://www.bibsonomy.org/author/Wissner-Gross">Wissner-Gross</a> </span><em>page825--829. </em>(<em>2006</em>)Sun Feb 10 02:19:38 CET 2008Advanced Learning Technologies, 2006. Sixth International Conference on825--829Preparation of Topical Reading Lists from the Link Structure of Wikipedia2006wikipedia information-retrieval link-mining Personalized reading preparation poses an important challenge for education and continuing education. Using a PageRank derivative and graph distance ordering, we show that personalized background reading lists can be generated automatically from the link structure of Wikipedia. We examine the operation of our new tool in professional, student, and interdisciplinary researcher learning models. Additionally, we present desktop and mobile interfaces for the generated reading lists.stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/203c2bd7998e1f49454c32226907b8520/brightbytebrightbyte2008-02-10T02:19:38+01:00semantic wikipedia WW-MUST READ synonym information-retrieval <span style="color:#555555;">Maria <a href="http://www.bibsonomy.org/author/Ruiz-Casado">Ruiz-Casado</a> and Enrique <a href="http://www.bibsonomy.org/author/Alfonseca">Alfonseca</a> and Pablo <a href="http://www.bibsonomy.org/author/Castells">Castells</a> </span>(<em>2005</em>)Sun Feb 10 02:19:38 CET 2008Advances in Web Intelligence380--386Automatic Assignment of Wikipedia Encyclopedic Entries to WordNet Synsets2005semantic wikipedia WW-MUST READ synonym information-retrieval We describe an approach taken for automatically associating entries from an on-line encyclopedia with concepts in an ontology or a lexical semantic network. It has been tested with the Simple English Wikipedia and WordNet, although it can be used with other resources. The accuracy in disambiguating the sense of the encyclopedia entries reaches 91.11\% (83.89\% for polysemous words). It will be applied to enriching ontologies with encyclopedic knowledge.stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2f4ef8711a590bc02cce4634eac34c680/brightbytebrightbyte2008-02-10T02:19:38+01:00text-mining information-retrieval WW-MUST wikipedia <span style="color:#555555;">E. <a href="http://www.bibsonomy.org/author/Gabrilovich">Gabrilovich</a> and S. <a href="http://www.bibsonomy.org/author/Markovitch">Markovitch</a> </span><em>Proceedings of the Twenty-First National Conference on Artificial Intelligence, Boston, MA</em>(<em>2006</em>)Sun Feb 10 02:19:38 CET 2008Proceedings of the Twenty-First National Conference on Artificial Intelligence, Boston, MA{Overcoming the brittleness bottleneck using Wikipedia: Enhancing text categorization with encyclopedic knowledge}2006text-mining information-retrieval WW-MUST wikipedia stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/220c834420d2a014d99a32d441854c681/brightbytebrightbyte2008-02-10T02:19:38+01:00information-retrieval text-mining wikipedia <span style="color:#555555;">Gang <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and Huajie <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Haofen <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and Yong <a href="http://www.bibsonomy.org/author/Yu">Yu</a> </span>(<em>2007</em>)Sun Feb 10 02:19:38 CET 2008Natural Language Processing and Information Systems329--340Enhancing Relation Extraction by Eliciting Selectional Constraint Features from Wikipedia2007information-retrieval text-mining wikipedia Selectional Con straints are usually checked for detecting semantic relations. Previous work usually defined the constraints manually based on hand crafted concept taxonomy, which is time-consuming and impractical for large scale relation extraction. Further, the determination of entity type (e.g. NER) based on the taxonomy cannot achieve sufficiently high accuracy. In this paper, we propose a novel approach to extracting relation instances using the features elicited from Wikipedia, a free online encyclopedia. The features are represented as selectional constraints and further employed to enhance the extrac tion of relations. We conduct case stud ies on the validation of the ex tracted instances for two common relations hasAr tist(album, artist) andhasDirector(film, director). Substantially high extraction precision (around 0.95) and validation accuracy (near 0.90) are obtained.stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2034b007e0f95c91937337f4d7bd66223/brightbytebrightbyte2008-02-10T02:19:38+01:00wikipedia information-retrieval text-mining <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Sebastian <a href="http://www.bibsonomy.org/author/Blohm">Blohm</a> </span><em>International Workshop on Intelligent Information Access, </em><em>Helsinki, </em><em>JulyJune--August2006. </em>Sun Feb 10 02:19:38 CET 2008HelsinkiInternational Workshop on Intelligent Information AccessJulyJune--AugustA Self Organizing Map for Relation Extraction from Wikipedia using Structured Data Representations2006wikipedia information-retrieval text-mining stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/2998cdd9cce618c17a1b35d340cded4cc/brightbytebrightbyte2008-02-10T02:19:38+01:00wikipedia information-retrieval datamining READ semantic text-mining WW-MUST <span style="color:#555555;">David <a href="http://www.bibsonomy.org/author/Milne">Milne</a> and Olena <a href="http://www.bibsonomy.org/author/Medelyan">Medelyan</a> and Ian H. <a href="http://www.bibsonomy.org/author/Witten">Witten</a> </span><em>ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06), </em><em>page442--448. </em>(<em>2006</em>)Sun Feb 10 02:19:38 CET 2008ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)442--448Mining Domain-Specific Thesauri from Wikipedia: A case study2006wikipedia information-retrieval datamining READ semantic text-mining WW-MUST stuff from citeyoulikehttp://www.bibsonomy.org/bibtex/242b35b1fbc7dd4d20894ff9c92e2d6b5/brightbytebrightbyte2008-02-10T02:19:38+01:00wikipedia information-retrieval link-mining <span style="color:#555555;">Wissner A. D. <a href="http://www.bibsonomy.org/author/Gross">Gross</a> </span><em>page825--829. </em>(<em>2006</em>)Sun Feb 10 02:19:38 CET 2008Advanced Learning Technologies, 2006. Sixth International Conference on825--829Preparation of Topical Reading Lists from the Link Structure of Wikipedia2006wikipedia information-retrieval link-mining Personalized reading preparation poses an important challenge for education and continuing education. Using a PageRank derivative and graph distance ordering, we show that personalized background reading lists can be generated automatically from the link structure of Wikipedia. We examine the operation of our new tool in professional, student, and interdisciplinary researcher learning models. Additionally, we present desktop and mobile interfaces for the generated reading lists.