Search (5 results, page 1 of 1)

0.0059131454 = product of:
  0.029565725 = sum of:
    0.029565725 = product of:
      0.05913145 = sum of:
        0.05913145 = weight(_text_:etc in 2848) [ClassicSimilarity], result of:
          0.05913145 = score(doc=2848,freq=2.0), product of:
            0.19761753 = queryWeight, product of:
              5.4164915 = idf(docFreq=533, maxDocs=44218)
              0.036484417 = queryNorm
            0.2992217 = fieldWeight in 2848, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4164915 = idf(docFreq=533, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2848)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

In this article we present FLUX-CiM, a novel method for extracting components (e.g., author names, article titles, venues, page numbers) from bibliographic citations. Our method does not rely on patterns encoding specific delimiters used in a particular citation style. This feature yields a high degree of automation and flexibility, and allows FLUX-CiM to extract from citations in any given format. Differently from previous methods that are based on models learned from user-driven training, our method relies on a knowledge base automatically constructed from an existing set of sample metadata records from a given field (e.g., computer science, health sciences, social sciences, etc.). These records are usually available on the Web or other public data repositories. To demonstrate the effectiveness and applicability of our proposed method, we present a series of experiments in which we apply it to extract bibliographic data from citations in articles of different fields. Results of these experiments exhibit precision and recall levels above 94% for all fields, and perfect extraction for the large majority of citations tested. In addition, in a comparison against a state-of-the-art information-extraction method, ours produced superior results without the training phase required by that method. Finally, we present a strategy for using bibliographic data resulting from the extraction process with FLUX-CiM to automatically update and expand the knowledge base of a given domain. We show that this strategy can be used to achieve good extraction results even if only a very small initial sample of bibliographic records is available for building the knowledge base.

0.0041203364 = product of:
  0.02060168 = sum of:
    0.02060168 = product of:
      0.04120336 = sum of:
        0.04120336 = weight(_text_:problems in 3587) [ClassicSimilarity], result of:
          0.04120336 = score(doc=3587,freq=2.0), product of:
            0.15058853 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.036484417 = queryNorm
            0.27361554 = fieldWeight in 3587, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.046875 = fieldNorm(doc=3587)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

The vast majority of the current author name disambiguation solutions are designed to disambiguate a whole digital library (DL) at once considering the entire repository. However, these solutions besides being very expensive and having scalability problems, also may not benefit from eventual manual corrections, as they may be lost whenever the process of disambiguating the entire repository is required. In the real world, in which repositories are updated on a daily basis, incremental solutions that disambiguate only the newly introduced citation records, are likely to produce improved results in the long run. However, the problem of incremental author name disambiguation has been largely neglected in the literature. In this article we present a new author name disambiguation method, specially designed for the incremental scenario. In our experiments, our new method largely outperforms recent incremental proposals reported in the literature as well as the current state-of-the-art non-incremental method.

0.0034336136 = product of:
  0.017168067 = sum of:
    0.017168067 = product of:
      0.034336135 = sum of:
        0.034336135 = weight(_text_:problems in 937) [ClassicSimilarity], result of:
          0.034336135 = score(doc=937,freq=2.0), product of:
            0.15058853 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.036484417 = queryNorm
            0.22801295 = fieldWeight in 937, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0390625 = fieldNorm(doc=937)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

In this article, we elaborate on the meaning of quality in digital libraries (DLs) by proposing a model that is deeply grounded in a formal framework for digital libraries: 5S (Streams, Structures, Spaces, Scenarios, and Societies). For each major DL concept in the framework we formally define a number of dimensions of quality and propose a set of numerical indicators for those quality dimensions. In particular, we consider key concepts of a minimal DL: catalog, collection, digital object, metadata specification, repository, and services. Regarding quality dimensions, we consider: accessibility, accuracy, completeness, composability, conformance, consistency, effectiveness, efficiency, extensibility, pertinence, preservability, relevance, reliability, reusability, significance, similarity, and timeliness. Regarding measurement, we consider characteristics like: response time (with regard to efficiency), cost of migration (with respect to preservability), and number of service failures (to assess reliability). For some key DL concepts, the (quality dimension, numerical indicator) pairs are illustrated through their application to a number of "real-world" digital libraries. We also discuss connections between the proposed dimensions of DL quality and an expanded version of a workshop's consensus view of the life cycle of information in digital libraries. Such connections can be used to determine when and where quality issues can be measured, assessed, and improved - as well as how possible quality problems can be prevented, detected, and eliminated.

0.0024715676 = product of:
  0.0123578375 = sum of:
    0.0123578375 = product of:
      0.024715675 = sum of:
        0.024715675 = weight(_text_:22 in 3343) [ClassicSimilarity], result of:
          0.024715675 = score(doc=3343,freq=2.0), product of:
            0.12776221 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.036484417 = queryNorm
            0.19345059 = fieldWeight in 3343, 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=3343)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Date

16.11.2017 13:04:22

0.0024715676 = product of:
  0.0123578375 = sum of:
    0.0123578375 = product of:
      0.024715675 = sum of:
        0.024715675 = weight(_text_:22 in 3524) [ClassicSimilarity], result of:
          0.024715675 = score(doc=3524,freq=2.0), product of:
            0.12776221 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.036484417 = queryNorm
            0.19345059 = fieldWeight in 3524, 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=3524)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Date

16.11.2017 13:30:22