Search (84 results, page 1 of 5)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.05
    0.04586705 = product of:
      0.13760114 = sum of:
        0.09430586 = weight(_text_:computer in 1352) [ClassicSimilarity], result of:
          0.09430586 = score(doc=1352,freq=4.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.5710171 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
        0.04329528 = product of:
          0.08659056 = sum of:
            0.08659056 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
              0.08659056 = score(doc=1352,freq=4.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.54716086 = fieldWeight in 1352, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.078125 = fieldNorm(doc=1352)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
    Series
    Lecture notes in computer science; 2539
  2. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.05
    0.04540606 = product of:
      0.13621818 = sum of:
        0.09335803 = weight(_text_:computer in 2134) [ClassicSimilarity], result of:
          0.09335803 = score(doc=2134,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.56527805 = fieldWeight in 2134, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.109375 = fieldNorm(doc=2134)
        0.04286014 = product of:
          0.08572028 = sum of:
            0.08572028 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
              0.08572028 = score(doc=2134,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.5416616 = fieldWeight in 2134, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.109375 = fieldNorm(doc=2134)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    30. 3.2001 13:32:22
    Source
    Computer journal. 26(1983), S.239-246
  3. Faaborg, A.; Lagoze, C.: Semantic browsing (2003) 0.05
    0.04537056 = product of:
      0.13611168 = sum of:
        0.046679016 = weight(_text_:computer in 1026) [ClassicSimilarity], result of:
          0.046679016 = score(doc=1026,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.28263903 = fieldWeight in 1026, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1026)
        0.08943266 = sum of:
          0.046572514 = weight(_text_:resources in 1026) [ClassicSimilarity], result of:
            0.046572514 = score(doc=1026,freq=2.0), product of:
              0.16496566 = queryWeight, product of:
                3.650338 = idf(docFreq=3122, maxDocs=44218)
                0.045191888 = queryNorm
              0.28231642 = fieldWeight in 1026, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.650338 = idf(docFreq=3122, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1026)
          0.04286014 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
            0.04286014 = score(doc=1026,freq=2.0), product of:
              0.1582543 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.045191888 = queryNorm
              0.2708308 = fieldWeight in 1026, 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=1026)
      0.33333334 = coord(2/6)
    
    Abstract
    We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.
    Series
    Lecture notes in computer science; vol.2769
    Source
    Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
  4. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.04
    0.041345302 = product of:
      0.1240359 = sum of:
        0.040010586 = weight(_text_:computer in 5704) [ClassicSimilarity], result of:
          0.040010586 = score(doc=5704,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.24226204 = fieldWeight in 5704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
        0.084025316 = weight(_text_:network in 5704) [ClassicSimilarity], result of:
          0.084025316 = score(doc=5704,freq=4.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.41750383 = fieldWeight in 5704, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
      0.33333334 = coord(2/6)
    
    Abstract
    Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
  5. Drexel, G.: Knowledge engineering for intelligent information retrieval (2001) 0.03
    0.033141818 = product of:
      0.09942545 = sum of:
        0.040010586 = weight(_text_:computer in 4043) [ClassicSimilarity], result of:
          0.040010586 = score(doc=4043,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.24226204 = fieldWeight in 4043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.046875 = fieldNorm(doc=4043)
        0.059414867 = weight(_text_:network in 4043) [ClassicSimilarity], result of:
          0.059414867 = score(doc=4043,freq=2.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.29521978 = fieldWeight in 4043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=4043)
      0.33333334 = coord(2/6)
    
    Abstract
    This paper presents a clustered approach to designing an overall ontological model together with a general rule-based component that serves as a mapping device. By observational criteria, a multi-lingual team of experts excerpts concepts from general communication in the media. The team, then, finds equivalent expressions in English, German, French, and Spanish. On the basis of a set of ontological and lexical relations, a conceptual network is built up. Concepts are thought to be universal. Objects unique in time and space are identified by names and will be explained by the universals as their instances. Our approach relies on multi-relational descriptions of concepts. It provides a powerful tool for documentation and conceptual language learning. First and foremost, our multi-lingual, polyhierarchical ontology fills the gap of semantically-based information retrieval by generating enhanced and improved queries for internet search
    Series
    Lecture notes in computer science; vol.2004
  6. Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003) 0.03
    0.032679323 = product of:
      0.098037966 = sum of:
        0.079219826 = weight(_text_:network in 1254) [ClassicSimilarity], result of:
          0.079219826 = score(doc=1254,freq=8.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.3936264 = fieldWeight in 1254, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
        0.018818138 = product of:
          0.037636276 = sum of:
            0.037636276 = weight(_text_:resources in 1254) [ClassicSimilarity], result of:
              0.037636276 = score(doc=1254,freq=4.0), product of:
                0.16496566 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.045191888 = queryNorm
                0.22814612 = fieldWeight in 1254, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.03125 = fieldNorm(doc=1254)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Abstract
    Knowledge management in digital libraries is a universal problem. Keyword-based searching is applied everywhere no matter whether the resources are indexed databases or full-text Web pages. In keyword matching, the valuable content description and indexing of the metadata, such as the subject descriptors and the classification notations, are merely treated as common keywords to be matched with the user query. Without the support of vocabulary control tools, such as classification systems and thesauri, the intelligent labor of content analysis, description and indexing in metadata production are seriously wasted. New retrieval paradigms are needed to exploit the potential of the metadata resources. Could classification and thesauri, which contain the condensed intelligence of generations of librarians, be used in a digital library to organize the networked information, especially metadata, to facilitate their usability and change the digital library into a knowledge management environment? To examine that question, we designed and implemented a new paradigm that incorporates a classification system, a thesaurus and metadata. The classification and the thesaurus are merged into a concept network, and the metadata are distributed into the nodes of the concept network according to their subjects. The abstract concept node instantiated with the related metadata records becomes a knowledge node. A coherent and consistent knowledge network is thus formed. It is not only a framework for resource organization but also a structure for knowledge navigation, retrieval and learning. We have built an experimental system based on the Chinese Classification and Thesaurus, which is the most comprehensive and authoritative in China, and we have incorporated more than 5000 bibliographic records in the computing domain from the Peking University Library. The result is encouraging. In this article, we review the tools, the architecture and the implementation of our experimental system, which is called Vision.
  7. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.03
    0.0324329 = product of:
      0.0972987 = sum of:
        0.06668431 = weight(_text_:computer in 2751) [ClassicSimilarity], result of:
          0.06668431 = score(doc=2751,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.40377006 = fieldWeight in 2751, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.078125 = fieldNorm(doc=2751)
        0.030614385 = product of:
          0.06122877 = sum of:
            0.06122877 = weight(_text_:22 in 2751) [ClassicSimilarity], result of:
              0.06122877 = score(doc=2751,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.38690117 = fieldWeight in 2751, 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=2751)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  8. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.03
    0.0324329 = product of:
      0.0972987 = sum of:
        0.06668431 = weight(_text_:computer in 2754) [ClassicSimilarity], result of:
          0.06668431 = score(doc=2754,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.40377006 = fieldWeight in 2754, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.078125 = fieldNorm(doc=2754)
        0.030614385 = product of:
          0.06122877 = sum of:
            0.06122877 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
              0.06122877 = score(doc=2754,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.38690117 = fieldWeight in 2754, 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=2754)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  9. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.03
    0.0324329 = product of:
      0.0972987 = sum of:
        0.06668431 = weight(_text_:computer in 3279) [ClassicSimilarity], result of:
          0.06668431 = score(doc=3279,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.40377006 = fieldWeight in 3279, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.078125 = fieldNorm(doc=3279)
        0.030614385 = product of:
          0.06122877 = sum of:
            0.06122877 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
              0.06122877 = score(doc=3279,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = 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)
      0.33333334 = coord(2/6)
    
    Series
    Communications in computer and information science; 672
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  10. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.03
    0.0324329 = product of:
      0.0972987 = sum of:
        0.06668431 = weight(_text_:computer in 3280) [ClassicSimilarity], result of:
          0.06668431 = score(doc=3280,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.40377006 = fieldWeight in 3280, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.078125 = fieldNorm(doc=3280)
        0.030614385 = product of:
          0.06122877 = sum of:
            0.06122877 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
              0.06122877 = score(doc=3280,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = 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)
      0.33333334 = coord(2/6)
    
    Series
    Communications in computer and information science; 672
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  11. Bettencourt, N.; Silva, N.; Barroso, J.: Semantically enhancing recommender systems (2016) 0.03
    0.032155 = product of:
      0.096465 = sum of:
        0.040010586 = weight(_text_:computer in 3374) [ClassicSimilarity], result of:
          0.040010586 = score(doc=3374,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.24226204 = fieldWeight in 3374, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.046875 = fieldNorm(doc=3374)
        0.056454413 = product of:
          0.112908825 = sum of:
            0.112908825 = weight(_text_:resources in 3374) [ClassicSimilarity], result of:
              0.112908825 = score(doc=3374,freq=16.0), product of:
                0.16496566 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.045191888 = queryNorm
                0.68443835 = fieldWeight in 3374, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3374)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Abstract
    As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain's specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource's content, user's preferences, users' social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources' semantic information. The recommendation engine allows the promotion and discovery of unknown-unknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources.
    Series
    Communications in computer and information science; 631
  12. Baofu, P.: ¬The future of information architecture : conceiving a better way to understand taxonomy, network, and intelligence (2008) 0.03
    0.03118126 = product of:
      0.093543775 = sum of:
        0.0700211 = weight(_text_:network in 2257) [ClassicSimilarity], result of:
          0.0700211 = score(doc=2257,freq=4.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.34791988 = fieldWeight in 2257, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2257)
        0.023522673 = product of:
          0.047045346 = sum of:
            0.047045346 = weight(_text_:resources in 2257) [ClassicSimilarity], result of:
              0.047045346 = score(doc=2257,freq=4.0), product of:
                0.16496566 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.045191888 = queryNorm
                0.28518265 = fieldWeight in 2257, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2257)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Abstract
    The Future of Information Architecture examines issues surrounding why information is processed, stored and applied in the way that it has, since time immemorial. Contrary to the conventional wisdom held by many scholars in human history, the recurrent debate on the explanation of the most basic categories of information (eg space, time causation, quality, quantity) has been misconstrued, to the effect that there exists some deeper categories and principles behind these categories of information - with enormous implications for our understanding of reality in general. To understand this, the book is organised in to four main parts: Part I begins with the vital question concerning the role of information within the context of the larger theoretical debate in the literature. Part II provides a critical examination of the nature of data taxonomy from the main perspectives of culture, society, nature and the mind. Part III constructively invesitgates the world of information network from the main perspectives of culture, society, nature and the mind. Part IV proposes six main theses in the authors synthetic theory of information architecture, namely, (a) the first thesis on the simpleness-complicatedness principle, (b) the second thesis on the exactness-vagueness principle (c) the third thesis on the slowness-quickness principle (d) the fourth thesis on the order-chaos principle, (e) the fifth thesis on the symmetry-asymmetry principle, and (f) the sixth thesis on the post-human stage.
    LCSH
    Information resources
    Subject
    Information resources
  13. Fieldhouse, M.; Hancock-Beaulieu, M.: ¬The design of a graphical user interface for a highly interactive information retrieval system (1996) 0.02
    0.02270303 = product of:
      0.06810909 = sum of:
        0.046679016 = weight(_text_:computer in 6958) [ClassicSimilarity], result of:
          0.046679016 = score(doc=6958,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.28263903 = fieldWeight in 6958, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6958)
        0.02143007 = product of:
          0.04286014 = sum of:
            0.04286014 = weight(_text_:22 in 6958) [ClassicSimilarity], result of:
              0.04286014 = score(doc=6958,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.2708308 = fieldWeight in 6958, 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=6958)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  14. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.02
    0.02270303 = product of:
      0.06810909 = sum of:
        0.046679016 = weight(_text_:computer in 1319) [ClassicSimilarity], result of:
          0.046679016 = score(doc=1319,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.28263903 = fieldWeight in 1319, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1319)
        0.02143007 = product of:
          0.04286014 = sum of:
            0.04286014 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
              0.04286014 = score(doc=1319,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.2708308 = fieldWeight in 1319, 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=1319)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
  15. Qu, R.; Fang, Y.; Bai, W.; Jiang, Y.: Computing semantic similarity based on novel models of semantic representation using Wikipedia (2018) 0.02
    0.022048479 = product of:
      0.066145435 = sum of:
        0.049512394 = weight(_text_:network in 5052) [ClassicSimilarity], result of:
          0.049512394 = score(doc=5052,freq=2.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.2460165 = fieldWeight in 5052, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5052)
        0.016633041 = product of:
          0.033266082 = sum of:
            0.033266082 = weight(_text_:resources in 5052) [ClassicSimilarity], result of:
              0.033266082 = score(doc=5052,freq=2.0), product of:
                0.16496566 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.045191888 = queryNorm
                0.20165458 = fieldWeight in 5052, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5052)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Abstract
    Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.
  16. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.02
    0.01945974 = product of:
      0.058379218 = sum of:
        0.040010586 = weight(_text_:computer in 2419) [ClassicSimilarity], result of:
          0.040010586 = score(doc=2419,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.24226204 = fieldWeight in 2419, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.046875 = fieldNorm(doc=2419)
        0.018368632 = product of:
          0.036737263 = sum of:
            0.036737263 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
              0.036737263 = score(doc=2419,freq=2.0), product of:
                0.1582543 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045191888 = queryNorm
                0.23214069 = fieldWeight in 2419, 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=2419)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Date
    16.11.2008 16:22:48
    Series
    Lecture notes in computer science; vol.3232
  17. Agarwal, N.K.: Exploring context in information behavior : seeker, situation, surroundings, and shared identities (2018) 0.02
    0.017864719 = product of:
      0.053594157 = sum of:
        0.02692043 = weight(_text_:services in 4992) [ClassicSimilarity], result of:
          0.02692043 = score(doc=4992,freq=2.0), product of:
            0.16591617 = queryWeight, product of:
              3.6713707 = idf(docFreq=3057, maxDocs=44218)
              0.045191888 = queryNorm
            0.1622532 = fieldWeight in 4992, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6713707 = idf(docFreq=3057, maxDocs=44218)
              0.03125 = fieldNorm(doc=4992)
        0.026673725 = weight(_text_:computer in 4992) [ClassicSimilarity], result of:
          0.026673725 = score(doc=4992,freq=2.0), product of:
            0.16515417 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.045191888 = queryNorm
            0.16150802 = fieldWeight in 4992, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.03125 = fieldNorm(doc=4992)
      0.33333334 = coord(2/6)
    
    Abstract
    The field of human information behavior runs the gamut of processes from the realization of a need or gap in understanding, to the search for information from one or more sources to fill that gap, to the use of that information to complete a task at hand or to satisfy a curiosity, as well as other behaviors such as avoiding information or finding information serendipitously. Designers of mechanisms, tools, and computer-based systems to facilitate this seeking and search process often lack a full knowledge of the context surrounding the search. This context may vary depending on the job or role of the person; individual characteristics such as personality, domain knowledge, age, gender, perception of self, etc.; the task at hand; the source and the channel and their degree of accessibility and usability; and the relationship that the seeker shares with the source. Yet researchers have yet to agree on what context really means. While there have been various research studies incorporating context, and biennial conferences on context in information behavior, there lacks a clear definition of what context is, what its boundaries are, and what elements and variables comprise context. In this book, we look at the many definitions of and the theoretical and empirical studies on context, and I attempt to map the conceptual space of context in information behavior. I propose theoretical frameworks to map the boundaries, elements, and variables of context. I then discuss how to incorporate these frameworks and variables in the design of research studies on context. We then arrive at a unified definition of context. This book should provide designers of search systems a better understanding of context as they seek to meet the needs and demands of information seekers. It will be an important resource for researchers in Library and Information Science, especially doctoral students looking for one resource that covers an exhaustive range of the most current literature related to context, the best selection of classics, and a synthesis of these into theoretical frameworks and a unified definition. The book should help to move forward research in the field by clarifying the elements, variables, and views that are pertinent. In particular, the list of elements to be considered, and the variables associated with each element will be extremely useful to researchers wanting to include the influences of context in their studies.
    Series
    Synthesis lectures on information concepts, retrieval, and services; 61
  18. Kwok, K.L.: ¬A network approach to probabilistic information retrieval (1995) 0.02
    0.017151598 = product of:
      0.10290959 = sum of:
        0.10290959 = weight(_text_:network in 5696) [ClassicSimilarity], result of:
          0.10290959 = score(doc=5696,freq=6.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.51133573 = fieldWeight in 5696, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=5696)
      0.16666667 = coord(1/6)
    
    Abstract
    Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve
  19. Prasad, A.R.D.; Madalli, D.P.: Faceted infrastructure for semantic digital libraries (2008) 0.02
    0.016761195 = product of:
      0.05028358 = sum of:
        0.03365054 = weight(_text_:services in 1905) [ClassicSimilarity], result of:
          0.03365054 = score(doc=1905,freq=2.0), product of:
            0.16591617 = queryWeight, product of:
              3.6713707 = idf(docFreq=3057, maxDocs=44218)
              0.045191888 = queryNorm
            0.2028165 = fieldWeight in 1905, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6713707 = idf(docFreq=3057, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1905)
        0.016633041 = product of:
          0.033266082 = sum of:
            0.033266082 = weight(_text_:resources in 1905) [ClassicSimilarity], result of:
              0.033266082 = score(doc=1905,freq=2.0), product of:
                0.16496566 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.045191888 = queryNorm
                0.20165458 = fieldWeight in 1905, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1905)
          0.5 = coord(1/2)
      0.33333334 = coord(2/6)
    
    Abstract
    Purpose - The paper aims to argue that digital library retrieval should be based on semantic representations and propose a semantic infrastructure for digital libraries. Design/methodology/approach - The approach taken is formal model based on subject representation for digital libraries. Findings - Search engines and search techniques have fallen short of user expectations as they do not give context based retrieval. Deploying semantic web technologies would lead to efficient and more precise representation of digital library content and hence better retrieval. Though digital libraries often have metadata of information resources which can be accessed through OAI-PMH, much remains to be accomplished in making digital libraries semantic web compliant. This paper presents a semantic infrastructure for digital libraries, that will go a long way in providing them and web based information services with products highly customised to users needs. Research limitations/implications - Here only a model for semantic infrastructure is proposed. This model is proposed after studying current user-centric, top-down models adopted in digital library service architectures. Originality/value - This paper gives a generic model for building semantic infrastructure for digital libraries. Faceted ontologies for digital libraries is just one approach. But the same may be adopted by groups working with different approaches in building ontologies to realise efficient retrieval in digital libraries.
  20. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.02
    0.016504131 = product of:
      0.09902479 = sum of:
        0.09902479 = weight(_text_:network in 2393) [ClassicSimilarity], result of:
          0.09902479 = score(doc=2393,freq=8.0), product of:
            0.2012564 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.045191888 = queryNorm
            0.492033 = fieldWeight in 2393, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.16666667 = coord(1/6)
    
    Abstract
    This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

Years

Languages

  • e 76
  • d 7

Types

  • a 73
  • el 10
  • m 6
  • s 1
  • x 1
  • More… Less…