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  • × year_i:[2000 TO 2010}
  1. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.46
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    Abstract
    Although service-oriented architectures go a long way toward providing interoperability in distributed, heterogeneous environments, managing semantic differences in such environments remains a challenge. We give an overview of the issue of semantic interoperability (integration), provide a semantic characterization of services, and discuss the role of ontologies. Then we analyze four basic models of semantic interoperability that differ in respect to their mapping between service descriptions and ontologies and in respect to where the evaluation of the integration logic is performed. We also provide some guidelines for selecting one of the possible interoperability models.
    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  2. Schrodt, R.: Tiefen und Untiefen im wissenschaftlichen Sprachgebrauch (2008) 0.42
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    Content
    Vgl. auch: https://studylibde.com/doc/13053640/richard-schrodt. Vgl. auch: http%3A%2F%2Fwww.univie.ac.at%2FGermanistik%2Fschrodt%2Fvorlesung%2Fwissenschaftssprache.doc&usg=AOvVaw1lDLDR6NFf1W0-oC9mEUJf.
  3. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.40
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    Abstract
    By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.
    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  4. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.39
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  5. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.39
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    Abstract
    The employees of an organization often use a personal hierarchical classification scheme to organize digital documents that are stored on their own workstations. As this may make it hard for other employees to retrieve these documents, there is a risk that the organization will lose track of needed documentation. Furthermore, the inherent boundaries of such a hierarchical structure require making arbitrary decisions about which specific criteria the classification will b.e based on (for instance, the administrative activity or the document type, although a document can have several attributes and require classification in several classes).A faceted classification model to support corporate information organization is proposed. Partially based on Ranganathan's facets theory, this model aims not only to standardize the organization of digital documents, but also to simplify the management of a document throughout its life cycle for both individuals and organizations, while ensuring compliance to regulatory and policy requirements.
    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  6. Donsbach, W.: Wahrheit in den Medien : über den Sinn eines methodischen Objektivitätsbegriffes (2001) 0.32
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    Source
    Politische Meinung. 381(2001) Nr.1, S.65-74 [https%3A%2F%2Fwww.dgfe.de%2Ffileadmin%2FOrdnerRedakteure%2FSektionen%2FSek02_AEW%2FKWF%2FPublikationen_Reihe_1989-2003%2FBand_17%2FBd_17_1994_355-406_A.pdf&usg=AOvVaw2KcbRsHy5UQ9QRIUyuOLNi]
    Theme
    Information
  7. Fan, W.; Gordon, M.D.; Pathak, P.: ¬A generic ranking function discovery framework by genetic programming for information retrieval (2004) 0.10
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    Abstract
    Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
    Source
    Information processing and management. 40(2004) no.4, S.587-602
  8. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.09
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    Date
    25. 8.2005 17:42:22
    Source
    Library and information research news. 24(2000) no.77, S.30-34
  9. Qin, T.; Zhang, X.-D.; Tsai, M.-F.; Wang, D.-S.; Liu, T.-Y.; Li, H.: Query-level loss functions for information retrieval (2008) 0.08
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    Abstract
    Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since originally the methods were not developed for this task, their loss functions do not directly link to the criteria used in the evaluation of ranking. Specifically, the loss functions are defined on the level of documents or document pairs, in contrast to the fact that the evaluation criteria are defined on the level of queries. Therefore, minimizing the loss functions does not necessarily imply enhancing ranking performances. To solve this problem, we propose using query-level loss functions in learning of ranking functions. We discuss the basic properties that a query-level loss function should have and propose a query-level loss function based on the cosine similarity between a ranking list and the corresponding ground truth. We further design a coordinate descent algorithm, referred to as RankCosine, which utilizes the proposed loss function to create a generalized additive ranking model. We also discuss whether the loss functions of existing ranking algorithms can be extended to query-level. Experimental results on the datasets of TREC web track, OHSUMED, and a commercial web search engine show that with the use of the proposed query-level loss function we can significantly improve ranking accuracies. Furthermore, we found that it is difficult to extend the document-level loss functions to query-level loss functions.
    Source
    Information processing and management. 44(2008) no.2, S.838-855
  10. Carpineto, C.; Romano, G.: Order-theoretical ranking (2000) 0.07
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    Abstract
    Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresses the word mismatch problem by exploiting interdocument similarity information in a novel way. Document ranking is seen as a query-document transformation driven by a conceptual representation of the whole document collection, into which the query is merged. Our approach is nased on the theory of concept (or Galois) lattices, which, er argue, provides a powerful, well-founded, and conputationally-tractable framework to model the space in which documents and query are represented and to compute such a transformation. We compared information retrieval using concept lattice-based ranking (CLR) to BMR and HCR. The results showed that HCR was outperformed by CLR as well as BMR, and suggested that, of the two best methods, BMR achieved better performance than CLR on the whole document set, whereas CLR compared more favorably when only the first retrieved documents were used for evaluation. We also evaluated the three methods' specific ability to rank documents that did not match the query, in which case the speriority of CLR over BMR and HCR was apparent
    Source
    Journal of the American Society for Information Science. 51(2000) no.7, S.587-601
  11. Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000) 0.07
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    Source
    Information retrieval. 3(2000), S.217-227
  12. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.07
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    Abstract
    Im Rahmen des klassischen Information Retrieval wurden verschiedene Verfahren für das Ranking sowie die Suche in einer homogenen strukturlosen Dokumentenmenge entwickelt. Die Erfolge der Suchmaschine Google haben gezeigt dass die Suche in einer zwar inhomogenen aber zusammenhängenden Dokumentenmenge wie dem Internet unter Berücksichtigung der Dokumentenverbindungen (Links) sehr effektiv sein kann. Unter den von der Suchmaschine Google realisierten Konzepten ist ein Verfahren zum Ranking von Suchergebnissen (PageRank), das in diesem Artikel kurz erklärt wird. Darüber hinaus wird auf die Konzepte eines Systems namens CiteSeer eingegangen, welches automatisch bibliographische Angaben indexiert (engl. Autonomous Citation Indexing, ACI). Letzteres erzeugt aus einer Menge von nicht vernetzten wissenschaftlichen Dokumenten eine zusammenhängende Dokumentenmenge und ermöglicht den Einsatz von Banking-Verfahren, die auf den von Google genutzten Verfahren basieren.
    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  13. Callan, J.: Distributed information retrieval (2000) 0.06
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    Abstract
    A multi-database model of distributed information retrieval is presented, in which people are assumed to have access to many searchable text databases. In such an environment, full-text information retrieval consists of discovering database contents, ranking databases by their expected ability to satisfy the query, searching a small number of databases, and merging results returned by different databases. This paper presents algorithms for each task. It also discusses how to reorganize conventional test collections into multi-database testbeds, and evaluation methodologies for multi-database experiments. A broad and diverse group of experimental results is presented to demonstrate that the algorithms are effective, efficient, robust, and scalable
    Series
    The Kluwer international series on information retrieval; 7
    Source
    Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft
  14. Alemayehu, N.: Analysis of performance variation using quey expansion (2003) 0.06
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    Abstract
    Information retrieval performance evaluation is commonly made based an the classical recall and precision based figures or graphs. However, important information indicating causes for variation may remain hidden under the average recall and precision figures. Identifying significant causes for variation can help researchers and developers to focus an opportunities for improvement that underlay the averages. This article presents a case study showing the potential of a statistical repeated measures analysis of variance for testing the significance of factors in retrieval performance variation. The TREC-9 Query Track performance data is used as a case study and the factors studied are retrieval method, topic, and their interaction. The results show that retrieval method, topic, and their interaction are all significant. A topic level analysis is also made to see the nature of variation in the performance of retrieval methods across topics. The observed retrieval performances of expansion runs are truly significant improvements for most of the topics. Analyses of the effect of query expansion an document ranking confirm that expansion affects ranking positively.
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.5, S.379-391
  15. Meho, L.I.; Sonnenwald, D.H.: Citation ranking versus peer evaluation of senior faculty research performance : a case study of Kurdish scholarship (2000) 0.06
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    Abstract
    The purpose of this study is to analyze the relationship between citation ranking and peer evaluation in assessing senior faculty research performance. Other studies typically derive their peer evaluation data directly from referees, often in the form of ranking. This study uses two additional sources of peer evaluation data: citation contant analysis and book review content analysis. 2 main questions are investigated: (a) To what degree does citation ranking correlate with data from citation content analysis, book reviews and peer ranking? (b) Is citation ranking a valif evaluative indicator of research performance of senior faculty members? This study shows that citation ranking can provide a valid indicator for comparative evaluation of senior faculty research performance
    Source
    Journal of the American Society for Information Science. 51(2000) no.2, S.123-138
  16. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.06
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    Abstract
    Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
    Date
    31. 5.2004 19:22:06
    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636
  17. Kaszkiel, M.; Zobel, J.: Effective ranking with arbitrary passages (2001) 0.06
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    Abstract
    Text retrieval systems store a great variety of documents, from abstracts, newspaper articles, and Web pages to journal articles, books, court transcripts, and legislation. Collections of diverse types of documents expose shortcomings in current approaches to ranking. Use of short fragments of documents, called passages, instead of whole documents can overcome these shortcomings: passage ranking provides convenient units of text to return to the user, can avoid the difficulties of comparing documents of different length, and enables identification of short blocks of relevant material among otherwise irrelevant text. In this article, we compare several kinds of passage in an extensive series of experiments. We introduce a new type of passage, overlapping fragments of either fixed or variable length. We show that ranking with these arbitrary passages gives substantial improvements in retrieval effectiveness over traditional document ranking schemes, particularly for queries on collections of long documents. Ranking with arbitrary passages shows consistent improvements compared to ranking with whole documents, and to ranking with previous passage types that depend on document structure or topic shifts in documents
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.4, S.344-364
  18. Stock, W.G.: On relevance distributions (2006) 0.06
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    Abstract
    There are at least three possible ways that documents are distributed by relevance: informetric (power law), inverse logistic, and dichotomous. The nature of the type of distribution has implications for the construction of relevance ranking algorithms for search engines, for automated (blind) relevance feedback, for user behavior when using Web search engines, for combining of outputs of search engines for metasearch, for topic detection and tracking, and for the methodology of evaluation of information retrieval systems.
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.8, S.1126-1129
  19. Lalmas, M.: XML retrieval (2009) 0.06
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    Abstract
    Documents usually have a content and a structure. The content refers to the text of the document, whereas the structure refers to how a document is logically organized. An increasingly common way to encode the structure is through the use of a mark-up language. Nowadays, the most widely used mark-up language for representing structure is the eXtensible Mark-up Language (XML). XML can be used to provide a focused access to documents, i.e. returning XML elements, such as sections and paragraphs, instead of whole documents in response to a query. Such focused strategies are of particular benefit for information repositories containing long documents, or documents covering a wide variety of topics, where users are directed to the most relevant content within a document. The increased adoption of XML to represent a document structure requires the development of tools to effectively access documents marked-up in XML. This book provides a detailed description of query languages, indexing strategies, ranking algorithms, presentation scenarios developed to access XML documents. Major advances in XML retrieval were seen from 2002 as a result of INEX, the Initiative for Evaluation of XML Retrieval. INEX, also described in this book, provided test sets for evaluating XML retrieval effectiveness. Many of the developments and results described in this book were investigated within INEX.
    Content
    Table of Contents: Introduction / Basic XML Concepts / Historical Perspectives / Query Languages / Indexing Strategies / Ranking Strategies / Presentation Strategies / Evaluating XML Retrieval Effectiveness / Conclusions
    LCSH
    Information retrieval
    Series
    Synthesis lectures on information concepts, retrieval & services; 7
    Subject
    Information retrieval
  20. Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003) 0.06
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    Abstract
    Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.6, S.528-548

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