Search (63 results, page 1 of 4)

0.19940174 = product of:
  0.2991026 = sum of:
    0.07477565 = product of:
      0.22432694 = sum of:
        0.22432694 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.22432694 = score(doc=400,freq=2.0), product of:
            0.39914545 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04708008 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.33333334 = coord(1/3)
    0.22432694 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.22432694 = score(doc=400,freq=2.0), product of:
        0.39914545 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04708008 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
  0.6666667 = coord(2/3)

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.17423193 = product of:
  0.2613479 = sum of:
    0.049850434 = product of:
      0.1495513 = sum of:
        0.1495513 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.1495513 = score(doc=5820,freq=2.0), product of:
            0.39914545 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04708008 = 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)
    0.21149747 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.21149747 = score(doc=5820,freq=4.0), product of:
        0.39914545 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04708008 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
  0.6666667 = coord(2/3)

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.

0.1329345 = product of:
  0.19940174 = sum of:
    0.049850434 = product of:
      0.1495513 = sum of:
        0.1495513 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.1495513 = score(doc=701,freq=2.0), product of:
            0.39914545 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04708008 = queryNorm
            0.3746787 = fieldWeight in 701, 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=701)
      0.33333334 = coord(1/3)
    0.1495513 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.1495513 = score(doc=701,freq=2.0), product of:
        0.39914545 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04708008 = queryNorm
        0.3746787 = fieldWeight in 701, 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=701)
  0.6666667 = coord(2/3)

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.

0.036996346 = product of:
  0.110989034 = sum of:
    0.110989034 = sum of:
      0.07909553 = weight(_text_:multimedia in 1434) [ClassicSimilarity], result of:
        0.07909553 = score(doc=1434,freq=4.0), product of:
          0.21832302 = queryWeight, product of:
            4.6372695 = idf(docFreq=1163, maxDocs=44218)
            0.04708008 = queryNorm
          0.3622867 = fieldWeight in 1434, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            4.6372695 = idf(docFreq=1163, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1434)
      0.031893507 = weight(_text_:22 in 1434) [ClassicSimilarity], result of:
        0.031893507 = score(doc=1434,freq=2.0), product of:
          0.16486642 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04708008 = queryNorm
          0.19345059 = fieldWeight in 1434, 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=1434)
  0.33333334 = coord(1/3)

Abstract

Knowledge organization of large-scale content on the Web requires substantial amounts of semantic metadata that is expensive to generate manually. Recent developments in Web technologies have enabled any user to tag documents and other forms of content thereby generating metadata that could help organize knowledge. However, merely adding one or more tags to a document is highly inadequate to capture the aboutness of the document and thereby to support powerful semantic functions such as automatic classification, question answering or true semantic search and retrieval. This is true even when the tags used are labels from a well-designed classification system such as a thesaurus or taxonomy. There is a strong need to develop a semantic tagging mechanism with sufficient expressive power to capture the aboutness of each part of a document or dataset or multimedia content in order to enable applications that can benefit from knowledge organization on the Web. This article proposes a highly expressive mechanism of using ontology snippets as semantic tags that map portions of a document or a part of a dataset or a segment of a multimedia content to concepts and relations in an ontology of the domain(s) of interest.

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

0.034877848 = product of:
  0.10463354 = sum of:
    0.10463354 = product of:
      0.20926708 = sum of:
        0.20926708 = weight(_text_:multimedia in 2801) [ClassicSimilarity], result of:
          0.20926708 = score(doc=2801,freq=28.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.9585204 = fieldWeight in 2801, product of:
              5.2915025 = tf(freq=28.0), with freq of:
                28.0 = termFreq=28.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2801)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The book covers multimedia ontology in heritage preservation with intellectual explorations of various themes of Indian cultural heritage. The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled. The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums. The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.

LCSH

Multimedia systems

Subject

Multimedia systems

Theme

Multimedia

0.030915909 = product of:
  0.092747726 = sum of:
    0.092747726 = product of:
      0.18549545 = sum of:
        0.18549545 = weight(_text_:multimedia in 5732) [ClassicSimilarity], result of:
          0.18549545 = score(doc=5732,freq=22.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.8496376 = fieldWeight in 5732, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5732)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).

Theme

Multimedia

0.025012203 = product of:
  0.07503661 = sum of:
    0.07503661 = product of:
      0.15007322 = sum of:
        0.15007322 = weight(_text_:multimedia in 5993) [ClassicSimilarity], result of:
          0.15007322 = score(doc=5993,freq=10.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.6873907 = fieldWeight in 5993, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.046875 = fieldNorm(doc=5993)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The lack of standardization in the production, organization and dissemination of information in documentation centers and institutions alike, as a result from the digitization of collections and their availability on the internet has called for integration efforts. The sheer availability of multimedia content has fostered the development of many distinct and, most of the time, independent metadata standards for its description. This study aims at presenting and comparing the existing standards of metadata, vocabularies and ontologies for multimedia annotation and also tries to offer a synthetic overview of its main strengths and weaknesses, aiding efforts for semantic integration and enhancing the findability of available multimedia resources on the web. We also aim at unveiling the characteristics that could, should and are perhaps not being highlighted in the characterization of multimedia resources.

0.023419332 = product of:
  0.07025799 = sum of:
    0.07025799 = sum of:
      0.044743184 = weight(_text_:multimedia in 4399) [ClassicSimilarity], result of:
        0.044743184 = score(doc=4399,freq=2.0), product of:
          0.21832302 = queryWeight, product of:
            4.6372695 = idf(docFreq=1163, maxDocs=44218)
            0.04708008 = queryNorm
          0.20494029 = fieldWeight in 4399, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.6372695 = idf(docFreq=1163, maxDocs=44218)
            0.03125 = fieldNorm(doc=4399)
      0.025514804 = weight(_text_:22 in 4399) [ClassicSimilarity], result of:
        0.025514804 = score(doc=4399,freq=2.0), product of:
          0.16486642 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04708008 = queryNorm
          0.15476047 = fieldWeight in 4399, 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=4399)
  0.33333334 = coord(1/3)

Abstract

Modular ontologies are built in modular manner by combining modules from multiple relevant ontologies. Ontology heterogeneity also arises during modular ontology construction because multiple ontologies are being dealt with, during this process. Ontologies need to be aligned before using them for modular ontology construction. The existing approaches for ontology alignment compare all the concepts of each ontology to be aligned, hence not optimized in terms of time and search space utilization. A new indexing technique is proposed based on modular ontology. An efficient ontology alignment technique is proposed to solve the heterogeneity problem during the construction of modular ontology. Results are satisfactory as Precision and Recall are improved by (8%) and (10%) respectively. The value of Pearsons Correlation Coefficient for degree of similarity, time, search space requirement, precision and recall are close to 1 which shows that the results are significant. Further research can be carried out for using modular ontology based indexing technique for Multimedia Information Retrieval and Bio-Medical information retrieval.

Date

20. 1.2015 18:30:22

0.011185795 = product of:
  0.033557385 = sum of:
    0.033557385 = product of:
      0.06711477 = sum of:
        0.06711477 = weight(_text_:multimedia in 4318) [ClassicSimilarity], result of:
          0.06711477 = score(doc=4318,freq=2.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.30741042 = fieldWeight in 4318, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.046875 = fieldNorm(doc=4318)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This paper presents a novel hybrid approach for visual information retrieval (VIR) that combines shape analysis of objects in image with their indexing by textual descriptions. The principal goal of presented technique is applying Two Segments Turning Function (2STF) proposed by authors for efficient invariant to spatial variations shape processing and implementation of semantic Web approaches for ontology-based user-oriented annotations of multimedia information. In the proposed approach the user's textual queries are converted to image features, which are used for images searching, indexing, interpretation, and retrieval. A decision about similarity between retrieved image and user's query is taken computing the shape convergence to 2STF combining it with matching the ontological annotations of objects in image and providing in this way automatic definition of the machine-understandable semantics. In order to evaluate the proposed approach the Image Retrieval by Ontological Description of Shapes system has been designed and tested using some standard image domains.

0.011185795 = product of:
  0.033557385 = sum of:
    0.033557385 = product of:
      0.06711477 = sum of:
        0.06711477 = weight(_text_:multimedia in 4865) [ClassicSimilarity], result of:
          0.06711477 = score(doc=4865,freq=2.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.30741042 = fieldWeight in 4865, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.046875 = fieldNorm(doc=4865)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Content Management Systeme (CMS) sind in vielen Organisationen bereits seit längerer Zeit fester Bestandteil zur Verwaltung und kollaborativen Bearbeitung von Text- und Multimedia-Inhalten. Im Zuge der rasch ansteigenden Fülle an Informationen und somit auch Wissen wird die Überschaubarkeit der Datenbestände jedoch massiv eingeschränkt. Diese und zusätzliche Anforderungen, wie automatisch Datenquellen aus dem World Wide Web (WWW) zu extrahieren, lassen traditionelle CMS immer mehr an ihre Grenzen stoßen. Dieser Beitrag diskutiert die neuen Herausforderungen an traditionelle CMS und bietet Lösungsvorschläge, wie CMS kombiniert mit semantischen Technologien diesen Herausforderungen begegnen können. Die Autoren stellen eine generische Systemarchitektur für Content Management Systeme vor, die einerseits Inhalte für das Semantic Web generieren, andererseits Content aus dem Web 2.0 syndizieren können und bei der Aufbereitung des Content den User mittels semantischer Technologien wie Reasoning oder Informationsextraktion unterstützen. Dabei wird auf Erfahrungen bei der prototypischen Implementierung von semantischer Technologie in ein bestehendes CMS System zurückgegriffen.

0.010631169 = product of:
  0.031893507 = sum of:
    0.031893507 = product of:
      0.06378701 = sum of:
        0.06378701 = weight(_text_:22 in 6089) [ClassicSimilarity], result of:
          0.06378701 = score(doc=6089,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.38690117 = fieldWeight in 6089, 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=6089)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Pages

S.11-22

0.010631169 = product of:
  0.031893507 = sum of:
    0.031893507 = product of:
      0.06378701 = sum of:
        0.06378701 = weight(_text_:22 in 5576) [ClassicSimilarity], result of:
          0.06378701 = score(doc=5576,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.38690117 = fieldWeight in 5576, 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=5576)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

13.12.2017 14:17:22

0.010631169 = product of:
  0.031893507 = sum of:
    0.031893507 = product of:
      0.06378701 = sum of:
        0.06378701 = weight(_text_:22 in 539) [ClassicSimilarity], result of:
          0.06378701 = score(doc=539,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.38690117 = fieldWeight in 539, 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=539)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

26.12.2011 13:22:07

0.010631169 = product of:
  0.031893507 = sum of:
    0.031893507 = product of:
      0.06378701 = sum of:
        0.06378701 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
          0.06378701 = score(doc=3406,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.38690117 = fieldWeight in 3406, 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=3406)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

30. 5.2010 16:22:35

0.010631169 = product of:
  0.031893507 = sum of:
    0.031893507 = product of:
      0.06378701 = sum of:
        0.06378701 = weight(_text_:22 in 4523) [ClassicSimilarity], result of:
          0.06378701 = score(doc=4523,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.38690117 = fieldWeight in 4523, 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=4523)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Spektrum der Wissenschaft. 2018, H.1, S.22-27

0.009321497 = product of:
  0.02796449 = sum of:
    0.02796449 = product of:
      0.05592898 = sum of:
        0.05592898 = weight(_text_:multimedia in 2669) [ClassicSimilarity], result of:
          0.05592898 = score(doc=2669,freq=2.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.25617537 = fieldWeight in 2669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2669)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.

0.009321497 = product of:
  0.02796449 = sum of:
    0.02796449 = product of:
      0.05592898 = sum of:
        0.05592898 = weight(_text_:multimedia in 3958) [ClassicSimilarity], result of:
          0.05592898 = score(doc=3958,freq=2.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.25617537 = fieldWeight in 3958, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3958)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Theme

Multimedia

0.009227812 = product of:
  0.027683435 = sum of:
    0.027683435 = product of:
      0.05536687 = sum of:
        0.05536687 = weight(_text_:multimedia in 1782) [ClassicSimilarity], result of:
          0.05536687 = score(doc=1782,freq=4.0), product of:
            0.21832302 = queryWeight, product of:
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.04708008 = queryNorm
            0.2536007 = fieldWeight in 1782, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.6372695 = idf(docFreq=1163, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1782)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

LCSH

Multimedia systems

Subject

Multimedia systems

0.009020846 = product of:
  0.027062537 = sum of:
    0.027062537 = product of:
      0.054125074 = sum of:
        0.054125074 = weight(_text_:22 in 3355) [ClassicSimilarity], result of:
          0.054125074 = score(doc=3355,freq=4.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.32829654 = fieldWeight in 3355, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3355)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

0.008504935 = product of:
  0.025514804 = sum of:
    0.025514804 = product of:
      0.051029608 = sum of:
        0.051029608 = weight(_text_:22 in 3376) [ClassicSimilarity], result of:
          0.051029608 = score(doc=3376,freq=2.0), product of:
            0.16486642 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04708008 = queryNorm
            0.30952093 = fieldWeight in 3376, 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=3376)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

31. 7.2010 16:58:22