Search (39 results, page 2 of 2)

0.005190944 = product of:
  0.07267321 = sum of:
    0.07267321 = sum of:
      0.056401204 = weight(_text_:texts in 1442) [ClassicSimilarity], result of:
        0.056401204 = score(doc=1442,freq=4.0), product of:
          0.16460659 = queryWeight, product of:
            5.4822793 = idf(docFreq=499, maxDocs=44218)
            0.03002521 = queryNorm
          0.34264246 = fieldWeight in 1442, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.4822793 = idf(docFreq=499, maxDocs=44218)
            0.03125 = fieldNorm(doc=1442)
      0.016272005 = weight(_text_:22 in 1442) [ClassicSimilarity], result of:
        0.016272005 = score(doc=1442,freq=2.0), product of:
          0.10514317 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03002521 = queryNorm
          0.15476047 = fieldWeight in 1442, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1442)
  0.071428575 = coord(1/14)

Abstract

The main objective of this research was to analyze whether there was a characteristic distribution behavior of relevant terms over a scientific text that could contribute as a criterion for their process of automatic indexing. The terms considered in this study were only full noun phrases contained in the texts themselves. The texts were considered a total of 98 doctoral theses of the eight areas of knowledge in a same university. Initially, 20 full noun phrases were automatically extracted from each text as candidates to be the most relevant terms, and each author of each text assigned a relevance value 0-6 (not relevant and highly relevant, respectively) for each of the 20 noun phrases sent. Only, 22.1 % of noun phrases were considered not relevant. A relevance values of the terms assigned by the authors were associated with their positions in the text. Each full noun phrases found in the text was considered as a valid linear position. The results that were obtained showed values resulting from this distribution by considering two types of position: linear, with values consolidated into ten equal consecutive parts; and structural, considering parts of the text (such as introduction, development and conclusion). As a result of considerable importance, all areas of knowledge related to the Natural Sciences showed a characteristic behavior in the distribution of relevant terms, as well as all areas of knowledge related to Social Sciences showed the same characteristic behavior of distribution, but distinct from the Natural Sciences. The difference of the distribution behavior between the Natural and Social Sciences can be clearly visualized through graphs. All behaviors, including the general behavior of all areas of knowledge together, were characterized in polynomial equations and can be applied in future as criteria for automatic indexing. Until the present date this work has become inedited of for two reasons: to present a method for characterizing the distribution of relevant terms in a scientific text, and also, through this method, pointing out a quantitative trait difference between the Natural and Social Sciences.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.004546677 = product of:
  0.06365348 = sum of:
    0.06365348 = weight(_text_:subject in 4292) [ClassicSimilarity], result of:
      0.06365348 = score(doc=4292,freq=18.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.5927426 = fieldWeight in 4292, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4292)
  0.071428575 = coord(1/14)

Abstract

Subject indexing plays an important role in supporting subject access to information resources. Current subject indexing systems do not make adequate distinctions on the importance of assigned subject descriptors. Assigning numeric weights to subject descriptors to distinguish their importance to the documents can strengthen the role of subject metadata. Automated methods are more cost-effective. This study compares different automated weighting methods in different environments. Two evaluation methods were used to assess the performance. Experiments on three datasets in the biomedical domain suggest the performance of different weighting methods depends on whether it is an abstract or full text environment. Mutual information with bag-of-words representation shows the best average performance in the full text environment, while cosine with bag-of-words representation is the best in an abstract environment. The cosine measure has relatively consistent and robust performance. A direct weighting method, IDF (Inverse Document Frequency), can produce quick and reasonable estimates of the weights. Bag-of-words representation generally outperforms the concept-based representation. Further improvement in performance can be obtained by using the learning-to-rank method to integrate different weighting methods. This study follows up Lu and Mao (Journal of the Association for Information Science and Technology, 66, 1776-1784, 2015), in which an automated weighted subject indexing method was proposed and validated. The findings from this study contribute to more effective weighted subject indexing.

0.0037123463 = product of:
  0.051972847 = sum of:
    0.051972847 = weight(_text_:subject in 4005) [ClassicSimilarity], result of:
      0.051972847 = score(doc=4005,freq=12.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.48397237 = fieldWeight in 4005, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4005)
  0.071428575 = coord(1/14)

Abstract

Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associated with the documents. On the other hand, the idea of probabilistic or weighted indexing was proposed a long time ago and has seen success in capturing uncertainties in the automatic indexing process. One hurdle to overcome in implementing weighted indexing in manual subject indexing systems is the practical burden that could be added to the already intensive indexing process. This study proposes a method to infer automatically the associations between subject terms and documents through text mining. By uncovering the connections between MeSH descriptors and document text, we are able to derive the weights of MeSH descriptors manually assigned to documents. Our initial results suggest that the inference method is feasible and promising. The study has practical implications for improving subject indexing practice and providing better support for information retrieval.

0.0034293185 = product of:
  0.048010457 = sum of:
    0.048010457 = weight(_text_:subject in 3434) [ClassicSimilarity], result of:
      0.048010457 = score(doc=3434,freq=16.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.4470745 = fieldWeight in 3434, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03125 = fieldNorm(doc=3434)
  0.071428575 = coord(1/14)

Abstract

The purpose of this study is to examine whether the understandings of subject-indexing processes conducted by human indexers have a positive impact on the effectiveness of automatic subject term assignment through text categorization (TC). More specifically, human indexers' subject-indexing approaches, or conceptions, in conjunction with semantic sources were explored in the context of a typical scientific journal article dataset. Based on the premise that subject indexing approaches or conceptions with semantic sources are important for automatic subject term assignment through TC, this study proposed an indexing conception-based framework. For the purpose of this study, two research questions were explored: To what extent are semantic sources effective? To what extent are indexing conceptions effective? The experiments were conducted using a Support Vector Machine implementation in WEKA (I.H. Witten & E. Frank, [2000]). Using F-measure, the experiment results showed that cited works, source title, and title were as effective as the full text while a keyword was found more effective than the full text. In addition, the findings showed that an indexing conception-based framework was more effective than the full text. The content-oriented and the document-oriented indexing approaches especially were found more effective than the full text. Among three indexing conception-based approaches, the content-oriented approach and the document-oriented approach were more effective than the domain-oriented approach. In other words, in the context of a typical scientific journal article dataset, the objective contents and authors' intentions were more desirable for automatic subject term assignment via TC than the possible users' needs. The findings of this study support that incorporation of human indexers' indexing approaches or conception in conjunction with semantic sources has a positive impact on the effectiveness of automatic subject term assignment.

0.0033645732 = product of:
  0.023552012 = sum of:
    0.011776006 = weight(_text_:classification in 1874) [ClassicSimilarity], result of:
      0.011776006 = score(doc=1874,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.12315229 = fieldWeight in 1874, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1874)
    0.011776006 = weight(_text_:classification in 1874) [ClassicSimilarity], result of:
      0.011776006 = score(doc=1874,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.12315229 = fieldWeight in 1874, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1874)
  0.14285715 = coord(2/14)

Content

"People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.

0.002848691 = product of:
  0.039881673 = sum of:
    0.039881673 = product of:
      0.079763345 = sum of:
        0.079763345 = weight(_text_:texts in 4913) [ClassicSimilarity], result of:
          0.079763345 = score(doc=4913,freq=2.0), product of:
            0.16460659 = queryWeight, product of:
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.03002521 = queryNorm
            0.4845696 = fieldWeight in 4913, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.0625 = fieldNorm(doc=4913)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

0.0024248946 = product of:
  0.033948522 = sum of:
    0.033948522 = weight(_text_:subject in 1016) [ClassicSimilarity], result of:
      0.033948522 = score(doc=1016,freq=8.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.31612942 = fieldWeight in 1016, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03125 = fieldNorm(doc=1016)
  0.071428575 = coord(1/14)

Abstract

Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. The primary goal of this research is the analysis of the contribution of thesaurus structure to the indexing process. The resulting models are evaluated in the context of automatic subject indexing using four collections of documents pre-indexed with 4 different thesauri (AGROVOC [UN Food and Agriculture Organization], high-energy physics taxonomy [HEP], National Agricultural Library Thesaurus [NALT], and medical subject headings [MeSH]). We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.

0.0021433241 = product of:
  0.030006537 = sum of:
    0.030006537 = weight(_text_:subject in 1518) [ClassicSimilarity], result of:
      0.030006537 = score(doc=1518,freq=4.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27942157 = fieldWeight in 1518, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1518)
  0.071428575 = coord(1/14)

Abstract

This new textbook examines the motivations and the different algorithms for automatic document summarization (ADS). We performed a recent state of the art. The book shows the main problems of ADS, difficulties and the solutions provided by the community. It presents recent advances in ADS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several exemples are included in order to clarify the theoretical concepts. The books currently available in the area of Automatic Document Summarization are not recent. Powerful algorithms have been developed in recent years that include several applications of ADS. The development of recent technology has impacted on the development of algorithms and their applications. The massive use of social networks and the new forms of the technology requires the adaptation of the classical methods of text summarizers. This is a new textbook on Automatic Text Summarization, based on teaching materials used in two or one-semester courses. It presents a extensive state-of-art and describes the new systems on the subject. Previous automatic summarization books have been either collections of specialized papers, or else authored books with only a chapter or two devoted to the field as a whole. In other hand, the classic books on the subject are not recent.

0.0021217826 = product of:
  0.029704956 = sum of:
    0.029704956 = weight(_text_:subject in 5236) [ClassicSimilarity], result of:
      0.029704956 = score(doc=5236,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27661324 = fieldWeight in 5236, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5236)
  0.071428575 = coord(1/14)

Abstract

The Black Book Interactive Project at the University of Kansas (KU) is developing an expanded corpus of novels by African American authors, with an emphasis on lesser known writers and a goal of expanding research in this field. Using a custom metadata schema with an emphasis on race-related elements, each novel is analyzed for a variety of elements such as literary style, targeted content analysis, historical context, and other areas. Librarians at KU have worked to develop a variety of computational text analysis processes designed to assist with specific aspects of this metadata collection, including text mining and natural language processing, automated subject extraction based on word sense disambiguation, harvesting data from Wikidata, and other actions.

0.0018186709 = product of:
  0.02546139 = sum of:
    0.02546139 = weight(_text_:subject in 4311) [ClassicSimilarity], result of:
      0.02546139 = score(doc=4311,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.23709705 = fieldWeight in 4311, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=4311)
  0.071428575 = coord(1/14)

Abstract

Automatic content indexing is one of the innovations that are increasingly changing the way libraries work. In theory, it promises a cataloguing service that would hardly be possible with humans in terms of speed, quantity and maybe quality. The German National Library (DNB) has also recognised this potential and is increasingly relying on the automatic indexing of their catalogue content. The DNB took a major step in this direction in 2017, which was announced in two papers. The announcement was rather restrained, but the content of the papers is all the more explosive for the library community: Since September 2017, the DNB has discontinued the intellectual indexing of series Band H and has switched to an automatic process for these series. The subject indexing of online publications (series O) has been purely automatical since 2010; from September 2017, monographs and periodicals published outside the publishing industry and university publications will no longer be indexed by people. This raises the question: What is the quality of the automatic indexing compared to the manual work or in other words to which degree can the automatic indexing replace people without a signi cant drop in regards to quality?

0.0017434291 = product of:
  0.024408007 = sum of:
    0.024408007 = product of:
      0.048816014 = sum of:
        0.048816014 = weight(_text_:22 in 5629) [ClassicSimilarity], result of:
          0.048816014 = score(doc=5629,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.46428138 = fieldWeight in 5629, 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=5629)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Source

B.I.T.online. 22(2019) H.2, S.163-166

0.0015155592 = product of:
  0.021217827 = sum of:
    0.021217827 = weight(_text_:subject in 3667) [ClassicSimilarity], result of:
      0.021217827 = score(doc=3667,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.19758089 = fieldWeight in 3667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3667)
  0.071428575 = coord(1/14)

Abstract

Descriptive metadata play a key role in finding relevant search results in large amounts of unstructured data. However, current scientific audiovisual media are provided with little metadata, which makes them hard to find, let alone individual sequences. In this paper, the TIB / AV-Portal is presented as a use case where methods concerning the automatic generation of metadata, a semantic search and cross-lingual retrieval (German/English) have already been applied. These methods result in a better discoverability of the scientific audiovisual media hosted in the portal. Text, speech, and image content of the video are automatically indexed by specialised GND (Gemeinsame Normdatei) subject headings. A semantic search is established based on properties of the GND ontology. The cross-lingual retrieval uses English 'translations' that were derived by an ontology mapping (DBpedia i. a.). Further ways of increasing the discoverability and reuse of the metadata are publishing them as Linked Open Data and interlinking them with other data sets.

0.0014528577 = product of:
  0.020340007 = sum of:
    0.020340007 = product of:
      0.040680014 = sum of:
        0.040680014 = weight(_text_:22 in 2759) [ClassicSimilarity], result of:
          0.040680014 = score(doc=2759,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.38690117 = fieldWeight in 2759, 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=2759)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Date

1. 2.2016 18:25:22

0.0011622861 = product of:
  0.016272005 = sum of:
    0.016272005 = product of:
      0.03254401 = sum of:
        0.03254401 = weight(_text_:22 in 401) [ClassicSimilarity], result of:
          0.03254401 = score(doc=401,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.30952093 = fieldWeight in 401, 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=401)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Date

11. 9.2012 19:43:22

0.0010170004 = product of:
  0.014238005 = sum of:
    0.014238005 = product of:
      0.02847601 = sum of:
        0.02847601 = weight(_text_:22 in 5195) [ClassicSimilarity], result of:
          0.02847601 = score(doc=5195,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.2708308 = fieldWeight in 5195, 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=5195)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Abstract

Große Aufmerksamkeit richtet sich im Moment auf das Potential von automatisierten Methoden in der Sacherschließung und deren Interaktionsmöglichkeiten mit intellektuellen Methoden. In diesem Kontext befasst sich der vorliegende Beitrag mit den folgenden Fragen: Was sind die Anforderungen an bibliothekarische Metadaten aus Sicht der Wissenschaft? Was wird gebraucht, um den Informationsbedarf der Fachcommunities zu bedienen? Und was bedeutet das entsprechend für die Automatisierung der Metadatenerstellung und -pflege? Dieser Beitrag fasst die von der Autorin eingenommene Position in einem Impulsvortrag und der Podiumsdiskussion beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

0.0010170004 = product of:
  0.014238005 = sum of:
    0.014238005 = product of:
      0.02847601 = sum of:
        0.02847601 = weight(_text_:22 in 5344) [ClassicSimilarity], result of:
          0.02847601 = score(doc=5344,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.2708308 = fieldWeight in 5344, 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=5344)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Abstract

Spätestens seit dem Deutschen Bibliothekartag 2018 hat sich die Diskussion zu den automatischen Verfahren der Inhaltserschließung der Deutschen Nationalbibliothek von einer politisch geführten Diskussion in eine Qualitätsdiskussion verwandelt. Der folgende Beitrag beschäftigt sich mit Fragen der Qualität von Inhaltserschließung in digitalen Zeiten, wo heterogene Erzeugnisse unterschiedlicher Verfahren aufeinandertreffen und versucht, wichtige Anforderungen an Qualität zu definieren. Dieser Tagungsbeitrag fasst die vom Autor als Impulse vorgetragenen Ideen beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV am 29. August 2018 in Kiel zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

8.7171455E-4 = product of:
  0.0122040035 = sum of:
    0.0122040035 = product of:
      0.024408007 = sum of:
        0.024408007 = weight(_text_:22 in 5628) [ClassicSimilarity], result of:
          0.024408007 = score(doc=5628,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.23214069 = fieldWeight in 5628, 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=5628)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Source

B.I.T.online. 22(2019) H.6, S.465-469

7.264289E-4 = product of:
  0.010170003 = sum of:
    0.010170003 = product of:
      0.020340007 = sum of:
        0.020340007 = weight(_text_:22 in 3780) [ClassicSimilarity], result of:
          0.020340007 = score(doc=3780,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.19345059 = fieldWeight in 3780, 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=3780)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Date

19. 8.2017 9:24:22

5.811431E-4 = product of:
  0.008136002 = sum of:
    0.008136002 = product of:
      0.016272005 = sum of:
        0.016272005 = weight(_text_:22 in 5499) [ClassicSimilarity], result of:
          0.016272005 = score(doc=5499,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.15476047 = fieldWeight in 5499, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=5499)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)

Date

20. 1.2015 18:30:22