Search (2209 results, page 1 of 111)

0.085812956 = sum of:
  0.069191486 = product of:
    0.20757446 = sum of:
      0.20757446 = weight(_text_:3a in 855) [ClassicSimilarity], result of:
        0.20757446 = score(doc=855,freq=2.0), product of:
          0.44320524 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.05227703 = queryNorm
          0.46834838 = fieldWeight in 855, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.0390625 = fieldNorm(doc=855)
    0.33333334 = coord(1/3)
  0.01662147 = product of:
    0.03324294 = sum of:
      0.03324294 = weight(_text_:research in 855) [ClassicSimilarity], result of:
        0.03324294 = score(doc=855,freq=4.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.22288933 = fieldWeight in 855, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0390625 = fieldNorm(doc=855)
    0.5 = coord(1/2)

Abstract

Converting UDC numbers manually to a complex format such as the one mentioned above is an unrealistic expectation; supporting building these representations, as far as possible automatically, is a well-founded requirement. An additional advantage of this approach is that the existing records could also be processed and converted. In my dissertation I would like to prove also that it is possible to design and implement an algorithm that is able to convert pre-coordinated UDC numbers into the introduced format by identifying all their elements and revealing their whole syntactic structure as well. In my dissertation I will discuss a feasible way of building a UDC-specific XML schema for describing the most detailed and complicated UDC numbers (containing not only the common auxiliary signs and numbers, but also the different types of special auxiliaries). The schema definition is available online at: http://piros.udc-interpreter.hu#xsd. The primary goal of my research is to prove that it is possible to support building, retrieving, and analyzing UDC numbers without compromises, by taking the whole syntactic richness of the scheme by storing the UDC numbers reserving the meaning of pre-coordination. The research has also included the implementation of a software that parses UDC classmarks attended to prove that such solution can be applied automatically without any additional effort or even retrospectively on existing collections.

Content

Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>

0.08302978 = product of:
  0.16605955 = sum of:
    0.16605955 = product of:
      0.49817866 = sum of:
        0.49817866 = weight(_text_:3a in 973) [ClassicSimilarity], result of:
          0.49817866 = score(doc=973,freq=2.0), product of:
            0.44320524 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.05227703 = queryNorm
            1.1240361 = fieldWeight in 973, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.09375 = fieldNorm(doc=973)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Content

Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.

0.07163884 = sum of:
  0.05535319 = product of:
    0.16605957 = sum of:
      0.16605957 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
        0.16605957 = score(doc=5820,freq=2.0), product of:
          0.44320524 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.05227703 = queryNorm
          0.3746787 = fieldWeight in 5820, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.03125 = fieldNorm(doc=5820)
    0.33333334 = coord(1/3)
  0.01628565 = product of:
    0.0325713 = sum of:
      0.0325713 = weight(_text_:research in 5820) [ClassicSimilarity], result of:
        0.0325713 = score(doc=5820,freq=6.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.21838607 = fieldWeight in 5820, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.03125 = fieldNorm(doc=5820)
    0.5 = coord(1/2)

Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.07070447 = product of:
  0.14140894 = sum of:
    0.14140894 = sum of:
      0.056415144 = weight(_text_:research in 2555) [ClassicSimilarity], result of:
        0.056415144 = score(doc=2555,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.37825575 = fieldWeight in 2555, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.09375 = fieldNorm(doc=2555)
      0.08499379 = weight(_text_:22 in 2555) [ClassicSimilarity], result of:
        0.08499379 = score(doc=2555,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.46428138 = fieldWeight in 2555, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.09375 = fieldNorm(doc=2555)
  0.5 = coord(1/2)

Date

15. 1.2016 19:22:28

0.07070447 = product of:
  0.14140894 = sum of:
    0.14140894 = sum of:
      0.056415144 = weight(_text_:research in 3276) [ClassicSimilarity], result of:
        0.056415144 = score(doc=3276,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.37825575 = fieldWeight in 3276, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.09375 = fieldNorm(doc=3276)
      0.08499379 = weight(_text_:22 in 3276) [ClassicSimilarity], result of:
        0.08499379 = score(doc=3276,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.46428138 = fieldWeight in 3276, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.09375 = fieldNorm(doc=3276)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.07070447 = product of:
  0.14140894 = sum of:
    0.14140894 = sum of:
      0.056415144 = weight(_text_:research in 4973) [ClassicSimilarity], result of:
        0.056415144 = score(doc=4973,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.37825575 = fieldWeight in 4973, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.09375 = fieldNorm(doc=4973)
      0.08499379 = weight(_text_:22 in 4973) [ClassicSimilarity], result of:
        0.08499379 = score(doc=4973,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.46428138 = fieldWeight in 4973, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.09375 = fieldNorm(doc=4973)
  0.5 = coord(1/2)

Date

1. 2.2019 12:50:22

0.069191486 = product of:
  0.13838297 = sum of:
    0.13838297 = product of:
      0.4151489 = sum of:
        0.4151489 = weight(_text_:3a in 1826) [ClassicSimilarity], result of:
          0.4151489 = score(doc=1826,freq=2.0), product of:
            0.44320524 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.05227703 = queryNorm
            0.93669677 = fieldWeight in 1826, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.078125 = fieldNorm(doc=1826)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

0.06168332 = product of:
  0.12336664 = sum of:
    0.12336664 = sum of:
      0.087952554 = weight(_text_:research in 1622) [ClassicSimilarity], result of:
        0.087952554 = score(doc=1622,freq=28.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.58970976 = fieldWeight in 1622, product of:
            5.2915025 = tf(freq=28.0), with freq of:
              28.0 = termFreq=28.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1622)
      0.03541408 = weight(_text_:22 in 1622) [ClassicSimilarity], result of:
        0.03541408 = score(doc=1622,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.19345059 = fieldWeight in 1622, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1622)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects. Design/methodology/approach - Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis. Findings - The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research. Research limitations/implications - The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research. Originality/value - The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.

Date

20. 1.2015 18:30:22

0.06168332 = product of:
  0.12336664 = sum of:
    0.12336664 = sum of:
      0.087952554 = weight(_text_:research in 4638) [ClassicSimilarity], result of:
        0.087952554 = score(doc=4638,freq=28.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.58970976 = fieldWeight in 4638, product of:
            5.2915025 = tf(freq=28.0), with freq of:
              28.0 = termFreq=28.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4638)
      0.03541408 = weight(_text_:22 in 4638) [ClassicSimilarity], result of:
        0.03541408 = score(doc=4638,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.19345059 = fieldWeight in 4638, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4638)
  0.5 = coord(1/2)

Abstract

Purpose Visualisations of research and research-related activities including research data management (RDM) as a lifecycle have proliferated in the last decade. The purpose of this paper is to offer a systematic analysis and critique of such models. Design/methodology/approach A framework for analysis synthesised from the literature presented and applied to nine examples. Findings The strengths of the lifecycle representation are to clarify stages in research and to capture key features of project-based research. Nevertheless, their weakness is that they typically mask various aspects of the complexity of research, constructing it as highly purposive, serial, uni-directional and occurring in a somewhat closed system. Other types of models such as spiral of knowledge creation or the data journey reveal other stories about research. It is suggested that we need to develop other metaphors and visualisations around research. Research limitations/implications The paper explores the strengths and weaknesses of the popular lifecycle model for research and RDM, and also considers alternative ways of representing them. Practical implications Librarians use lifecycle models to explain service offerings to users so the analysis will help them identify clearly the best type of representation for particular cases. The critique offered by the paper also reveals that because researchers do not necessarily identify with a lifecycle representation, alternative ways of representing research need to be developed. Originality/value The paper offers a systematic analysis of visualisations of research and RDM current in the Library and Information Studies literature revealing the strengths and weaknesses of the lifecycle metaphor.

Date

20. 1.2015 18:30:22

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 2090) [ClassicSimilarity], result of:
        0.047012623 = score(doc=2090,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 2090, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=2090)
      0.07082816 = weight(_text_:22 in 2090) [ClassicSimilarity], result of:
        0.07082816 = score(doc=2090,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 2090, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=2090)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3277) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3277,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3277, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3277)
      0.07082816 = weight(_text_:22 in 3277) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3277,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3277, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3277)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3278) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3278,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3278, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3278)
      0.07082816 = weight(_text_:22 in 3278) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3278,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3278, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3278)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3279) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3279,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3279, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3279)
      0.07082816 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3279,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3279, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3279)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3280) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3280,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3280, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3280)
      0.07082816 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3280,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3280, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3280)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3281) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3281,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3281, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3281)
      0.07082816 = weight(_text_:22 in 3281) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3281,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3281, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3281)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.05892039 = product of:
  0.11784078 = sum of:
    0.11784078 = sum of:
      0.047012623 = weight(_text_:research in 3282) [ClassicSimilarity], result of:
        0.047012623 = score(doc=3282,freq=2.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.31521314 = fieldWeight in 3282, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.078125 = fieldNorm(doc=3282)
      0.07082816 = weight(_text_:22 in 3282) [ClassicSimilarity], result of:
        0.07082816 = score(doc=3282,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.38690117 = fieldWeight in 3282, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.078125 = fieldNorm(doc=3282)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.057698686 = product of:
  0.11539737 = sum of:
    0.11539737 = sum of:
      0.06581766 = weight(_text_:research in 1568) [ClassicSimilarity], result of:
        0.06581766 = score(doc=1568,freq=8.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.44129837 = fieldWeight in 1568, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1568)
      0.04957971 = weight(_text_:22 in 1568) [ClassicSimilarity], result of:
        0.04957971 = score(doc=1568,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.2708308 = fieldWeight in 1568, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1568)
  0.5 = coord(1/2)

Abstract

Case Based Reasoning is a very important research trend in Artificial Intelligence and can be a powerful approach in the solution of complex problems characterized by heterogeneous knowledge. In this paper we present an ongoing research project where CBR is exploited to support the identification of enterprises potentially going to bankruptcy, through a comparison of their balance indexes with the ones of similar and already closed firms. In particular, the paper focuses on how developing similarity measures for strings can be profitably supported by metadata models of case structures and semantic methods like Query Expansion.

Pages

S.22-29

Source

Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al

0.05613746 = product of:
  0.11227492 = sum of:
    0.11227492 = sum of:
      0.06219185 = weight(_text_:research in 2590) [ClassicSimilarity], result of:
        0.06219185 = score(doc=2590,freq=14.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.41698778 = fieldWeight in 2590, product of:
            3.7416575 = tf(freq=14.0), with freq of:
              14.0 = termFreq=14.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2590)
      0.05008307 = weight(_text_:22 in 2590) [ClassicSimilarity], result of:
        0.05008307 = score(doc=2590,freq=4.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.27358043 = fieldWeight in 2590, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2590)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to analyse the global scientific outputs of ontology research, an important emerging discipline that has huge potential to improve information understanding, organization, and management. Design/methodology/approach - This study collected literature published during 1900-2012 from the Web of Science database. The bibliometric analysis was performed from authorial, institutional, national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic distribution, co-word analysis, and a new index were applied to the selected data. Findings - Characteristics of publication outputs suggested that ontology research has entered into the soaring stage, along with increased participation and collaboration. The authors identified the leading authors, institutions, nations, and articles in ontology research. Authors were more from North America, Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of frequently used keywords were identified: applications in Semantic Web, applications in bioinformatics, philosophy theories, and common supporting technology. Semantic Web research played a core role, and gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to information science. Originality/value - This is the first study to quantify global research patterns and trends in ontology, which might provide a potential guide for the future research. The new index provides an alternative way to evaluate the multidisciplinary influence of researchers.

Date

20. 1.2015 18:30:22
17. 9.2018 18:22:23

0.05579552 = product of:
  0.11159104 = sum of:
    0.11159104 = sum of:
      0.06909415 = weight(_text_:research in 2132) [ClassicSimilarity], result of:
        0.06909415 = score(doc=2132,freq=12.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.46326676 = fieldWeight in 2132, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.046875 = fieldNorm(doc=2132)
      0.042496894 = weight(_text_:22 in 2132) [ClassicSimilarity], result of:
        0.042496894 = score(doc=2132,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.23214069 = fieldWeight in 2132, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2132)
  0.5 = coord(1/2)

Abstract

The causal relation between research and economic growth is of particular importance for political support of science and technology as well as for academic purposes. This article revisits the causal relationship between research articles published and economic growth in Organisation for Economic Co-operation and Development (OECD) countries for the period 1981-2011, using bootstrap panel causality analysis, which accounts for cross-section dependency and heterogeneity across countries. The article, by the use of the specific method and the choice of the country group, makes a contribution to the existing literature. Our empirical results support unidirectional causality running from research output (in terms of total number of articles published) to economic growth for the US, Finland, Hungary, and Mexico; the opposite causality from economic growth to research articles published for Canada, France, Italy, New Zealand, the UK, Austria, Israel, and Poland; and no causality for the rest of the countries. Our findings provide important policy implications for research policies and strategies for OECD countries.

Date

8. 7.2015 22:00:42

0.054925613 = product of:
  0.109851226 = sum of:
    0.109851226 = sum of:
      0.053188704 = weight(_text_:research in 300) [ClassicSimilarity], result of:
        0.053188704 = score(doc=300,freq=4.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.35662293 = fieldWeight in 300, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.0625 = fieldNorm(doc=300)
      0.056662526 = weight(_text_:22 in 300) [ClassicSimilarity], result of:
        0.056662526 = score(doc=300,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.30952093 = fieldWeight in 300, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0625 = fieldNorm(doc=300)
  0.5 = coord(1/2)

Abstract

Purpose - This paper seeks to organize the extensive field and to compile the complete list of information limits. Design/methodology/approach - A thorough analysis of literature from the field beginning with the 1960s up to the present has been performed. Findings - A universal typology of information limits has been proposed. A list of barriers mentioned in the literature of the subject has been compiled. Research limitations/implications - The term "information limits" is not commonly used. Originality/value - The complete list of information limits with bibliographical hints (helpful for future research) is presented.

Date

12. 7.2011 18:22:52