Search (137 results, page 1 of 7)

0.031632032 = product of:
  0.1107121 = sum of:
    0.023607321 = weight(_text_:of in 3704) [ClassicSimilarity], result of:
      0.023607321 = score(doc=3704,freq=22.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.34381276 = fieldWeight in 3704, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
    0.08710478 = weight(_text_:distribution in 3704) [ClassicSimilarity], result of:
      0.08710478 = score(doc=3704,freq=2.0), product of:
        0.24019864 = queryWeight, product of:
          5.4703507 = idf(docFreq=505, maxDocs=44218)
          0.043909185 = queryNorm
        0.36263645 = fieldWeight in 3704, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.4703507 = idf(docFreq=505, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
  0.2857143 = coord(2/7)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634

0.029434524 = product of:
  0.10302083 = sum of:
    0.015916053 = weight(_text_:of in 1497) [ClassicSimilarity], result of:
      0.015916053 = score(doc=1497,freq=10.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.23179851 = fieldWeight in 1497, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1497)
    0.08710478 = weight(_text_:distribution in 1497) [ClassicSimilarity], result of:
      0.08710478 = score(doc=1497,freq=2.0), product of:
        0.24019864 = queryWeight, product of:
          5.4703507 = idf(docFreq=505, maxDocs=44218)
          0.043909185 = queryNorm
        0.36263645 = fieldWeight in 1497, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.4703507 = idf(docFreq=505, maxDocs=44218)
          0.046875 = fieldNorm(doc=1497)
  0.2857143 = coord(2/7)

Abstract

Text mining is about making serendipity more likely. Serendipity, the chance discovery of interesting ideas, has been responsible for many discoveries in science. Text mining systems strive to explore large text collections, separate the potentially meaningfull connections from a vast and mostly noisy background of random associations. In this paper we provide a summary of our text mining approach and also illustrate briefly some of the experiments we have conducted with this approach. In particular we use a profile-based text mining method. We have used these profiles to explore the global distribution of disease research, replicate discoveries made by others and propose new hypotheses. Text mining holds much potential that has yet to be tapped.

0.027124729 = product of:
  0.09493655 = sum of:
    0.02034102 = weight(_text_:of in 5481) [ClassicSimilarity], result of:
      0.02034102 = score(doc=5481,freq=12.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.29624295 = fieldWeight in 5481, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
    0.074595526 = weight(_text_:cataloging in 5481) [ClassicSimilarity], result of:
      0.074595526 = score(doc=5481,freq=4.0), product of:
        0.17305137 = queryWeight, product of:
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.043909185 = queryNorm
        0.43106002 = fieldWeight in 5481, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
  0.2857143 = coord(2/7)

Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.

Source

Cataloging and classification quarterly. 57(2019) no.5, S.315-336

0.018298242 = product of:
  0.06404384 = sum of:
    0.018832127 = weight(_text_:of in 720) [ClassicSimilarity], result of:
      0.018832127 = score(doc=720,freq=14.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.2742677 = fieldWeight in 720, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
    0.045211717 = weight(_text_:cataloging in 720) [ClassicSimilarity], result of:
      0.045211717 = score(doc=720,freq=2.0), product of:
        0.17305137 = queryWeight, product of:
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.043909185 = queryNorm
        0.26126182 = fieldWeight in 720, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
  0.2857143 = coord(2/7)

Abstract

This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.

Source

Cataloging and classification quarterly. 59(2021) no.8, p.815-834

0.016875139 = product of:
  0.059062984 = sum of:
    0.021386553 = weight(_text_:of in 5653) [ClassicSimilarity], result of:
      0.021386553 = score(doc=5653,freq=26.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.31146988 = fieldWeight in 5653, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.03767643 = weight(_text_:cataloging in 5653) [ClassicSimilarity], result of:
      0.03767643 = score(doc=5653,freq=2.0), product of:
        0.17305137 = queryWeight, product of:
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.043909185 = queryNorm
        0.21771818 = fieldWeight in 5653, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9411201 = idf(docFreq=2334, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
  0.2857143 = coord(2/7)

Abstract

Discusses the importance of knowledge organization in the context of the information overload caused by the vast quantities of data and information accessible on internal and external networks of an organization. Defines the characteristics of a knowledge-based product. Elaborates on the techniques and applications of text mining in developing knowledge products. Presents two approaches, as case studies, to the making of knowledge products: (1) steps and processes in the planning, designing and development of a composite multilingual multimedia CD product, with the potential international, inter-cultural end users in view, and (2) application of natural language processing software in text mining. Using a text mining software, it is possible to link concept terms from a processed text to a related thesaurus, glossary, schedules of a classification scheme, and facet structured subject representations. Concludes that the products of text mining and data mining could be made more useful if the features of a faceted scheme for subject classification are incorporated into text mining techniques and products.

Source

Cataloging and classification quarterly. 37(2003) nos.1/2, S.96-114

0.015502769 = product of:
  0.054259688 = sum of:
    0.01569344 = weight(_text_:of in 2533) [ClassicSimilarity], result of:
      0.01569344 = score(doc=2533,freq=14.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.22855641 = fieldWeight in 2533, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2533)
    0.038566247 = product of:
      0.07713249 = sum of:
        0.07713249 = weight(_text_:service in 2533) [ClassicSimilarity], result of:
          0.07713249 = score(doc=2533,freq=6.0), product of:
            0.18813887 = queryWeight, product of:
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.043909185 = queryNorm
            0.4099764 = fieldWeight in 2533, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2533)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Purpose - Since library storage has been increasing day by day, it is difficult for readers to find the books which interest them as well as representative booklists. How to utilize meaningful information effectively to improve the service quality of the digital library appears to be very important. The purpose of this paper is to provide a recommendation system architecture to promote digital library services in electronic libraries. Design/methodology/approach - In the proposed architecture, a two-phase data mining process used by association rule and clustering methods is designed to generate a recommendation system. The process considers not only the relationship of a cluster of users but also the associations among the information accessed. Findings - The process considered not only the relationship of a cluster of users but also the associations among the information accessed. With the advanced filter, the recommendation supported by the proposed system architecture would be closely served to meet users' needs. Originality/value - This paper not only constructs a recommendation service for readers to search books from the web but takes the initiative in finding the most suitable books for readers as well. Furthermore, library managers are expected to purchase core and hot books from a limited budget to maintain and satisfy the requirements of readers along with promoting digital library services.

0.014265795 = product of:
  0.049930282 = sum of:
    0.01423575 = weight(_text_:of in 6783) [ClassicSimilarity], result of:
      0.01423575 = score(doc=6783,freq=2.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.20732689 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.035694532 = product of:
      0.071389064 = sum of:
        0.071389064 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.071389064 = score(doc=6783,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.46428138 = fieldWeight in 6783, 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=6783)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

0.012226465 = product of:
  0.042792626 = sum of:
    0.021970814 = weight(_text_:of in 2908) [ClassicSimilarity], result of:
      0.021970814 = score(doc=2908,freq=14.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.31997898 = fieldWeight in 2908, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2908)
    0.02082181 = product of:
      0.04164362 = sum of:
        0.04164362 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.04164362 = score(doc=2908,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.2708308 = fieldWeight in 2908, 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=2908)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation

Source

Information systems. 22(1997) nos.5/6, S.349-385

0.012222101 = product of:
  0.042777352 = sum of:
    0.018981 = weight(_text_:of in 1737) [ClassicSimilarity], result of:
      0.018981 = score(doc=1737,freq=8.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.27643585 = fieldWeight in 1737, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.023796353 = product of:
      0.047592707 = sum of:
        0.047592707 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.047592707 = score(doc=1737,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.30952093 = fieldWeight in 1737, 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=1737)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it

Date

22.11.1998 18:57:22

0.011439489 = product of:
  0.04003821 = sum of:
    0.025165487 = weight(_text_:of in 5011) [ClassicSimilarity], result of:
      0.025165487 = score(doc=5011,freq=36.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.36650562 = fieldWeight in 5011, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5011)
    0.014872721 = product of:
      0.029745443 = sum of:
        0.029745443 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.029745443 = score(doc=5011,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.19345059 = fieldWeight in 5011, 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=5011)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Footnote

Beitrag eines Special issue on social informatics of knowledge

Source

Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411

0.011279118 = product of:
  0.039476912 = sum of:
    0.008304187 = weight(_text_:of in 3834) [ClassicSimilarity], result of:
      0.008304187 = score(doc=3834,freq=2.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.120940685 = fieldWeight in 3834, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3834)
    0.031172724 = product of:
      0.06234545 = sum of:
        0.06234545 = weight(_text_:service in 3834) [ClassicSimilarity], result of:
          0.06234545 = score(doc=3834,freq=2.0), product of:
            0.18813887 = queryWeight, product of:
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.043909185 = queryNorm
            0.33137995 = fieldWeight in 3834, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3834)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Offers brief accounts of Internet search tools. Covers the Lycos revamp; the new navigation service produced jointly by Excite and Netscape, delivering a language specific, locally relevant Web guide for Japan, Germany, France, the UK and Australia; InfoWatcher, a combination offline browser, search engine and push product from Carvelle Inc., USA; Alexa by Alexa Internet and WBI from IBM which are free and provide users with information on how others have used the Web sites which they are visiting; and Concept Explorer from Knowledge Discovery Systems, Inc., California which performs data mining from the Web, Usenet groups, MEDLINE and the US Patent and Trademark Office patent abstracts

0.010120074 = product of:
  0.035420258 = sum of:
    0.020547535 = weight(_text_:of in 1605) [ClassicSimilarity], result of:
      0.020547535 = score(doc=1605,freq=24.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.2992506 = fieldWeight in 1605, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.014872721 = product of:
      0.029745443 = sum of:
        0.029745443 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.029745443 = score(doc=1605,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.19345059 = fieldWeight in 1605, 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=1605)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

0.009667817 = product of:
  0.033837356 = sum of:
    0.007117875 = weight(_text_:of in 5565) [ClassicSimilarity], result of:
      0.007117875 = score(doc=5565,freq=2.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.103663445 = fieldWeight in 5565, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5565)
    0.02671948 = product of:
      0.05343896 = sum of:
        0.05343896 = weight(_text_:service in 5565) [ClassicSimilarity], result of:
          0.05343896 = score(doc=5565,freq=2.0), product of:
            0.18813887 = queryWeight, product of:
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.043909185 = queryNorm
            0.28403997 = fieldWeight in 5565, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.284727 = idf(docFreq=1655, maxDocs=44218)
              0.046875 = fieldNorm(doc=5565)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Source

Sprachtechnologie für eine dynamische Wirtschaft im Medienzeitalter - Language technologies for dynamic business in the age of the media - L'ingénierie linguistique au service de la dynamisation économique à l'ère du multimédia: Tagungsakten der XXVI. Jahrestagung der Internationalen Vereinigung Sprache und Wirtschaft e.V., 23.-25.11.2000, Fachhochschule Köln. Hrsg.: K.-D. Schmitz

0.009608563 = product of:
  0.03362997 = sum of:
    0.018757246 = weight(_text_:of in 668) [ClassicSimilarity], result of:
      0.018757246 = score(doc=668,freq=20.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.27317715 = fieldWeight in 668, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=668)
    0.014872721 = product of:
      0.029745443 = sum of:
        0.029745443 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.029745443 = score(doc=668,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.19345059 = fieldWeight in 668, 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=668)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.

Date

22. 3.2013 19:43:01

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586

0.00951053 = product of:
  0.033286855 = sum of:
    0.0094905 = weight(_text_:of in 1270) [ClassicSimilarity], result of:
      0.0094905 = score(doc=1270,freq=2.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.13821793 = fieldWeight in 1270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1270)
    0.023796353 = product of:
      0.047592707 = sum of:
        0.047592707 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.047592707 = score(doc=1270,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.30952093 = fieldWeight in 1270, 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=1270)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results

Source

Information systems. 22(1997) nos.5/6, S.333-347

0.007570464 = product of:
  0.026496623 = sum of:
    0.020547535 = weight(_text_:of in 1789) [ClassicSimilarity], result of:
      0.020547535 = score(doc=1789,freq=150.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.2992506 = fieldWeight in 1789, product of:
          12.247449 = tf(freq=150.0), with freq of:
            150.0 = termFreq=150.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.0059490884 = product of:
      0.011898177 = sum of:
        0.011898177 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.011898177 = score(doc=1789,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.07738023 = fieldWeight in 1789, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.015625 = fieldNorm(doc=1789)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

0.005949089 = product of:
  0.04164362 = sum of:
    0.04164362 = product of:
      0.08328724 = sum of:
        0.08328724 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.08328724 = score(doc=4577,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.5416616 = fieldWeight in 4577, 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=4577)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

2. 4.2000 18:01:22

0.0053168493 = product of:
  0.018608972 = sum of:
    0.0067107966 = weight(_text_:of in 1507) [ClassicSimilarity], result of:
      0.0067107966 = score(doc=1507,freq=4.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.09773483 = fieldWeight in 1507, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=1507)
    0.011898177 = product of:
      0.023796353 = sum of:
        0.023796353 = weight(_text_:22 in 1507) [ClassicSimilarity], result of:
          0.023796353 = score(doc=1507,freq=2.0), product of:
            0.15376249 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043909185 = queryNorm
            0.15476047 = fieldWeight in 1507, 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=1507)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Wir werden einmal die Grundlagen des Text-Mining-Systems bei IBM darstellen, dann werden wir das Projekt etwas umfangreicher und deutlicher darstellen, da kennen wir uns aus. Von daher haben wir zwei Teile, einmal Heidelberg, einmal Hamburg. Noch einmal zur Technologie. Text-Mining ist eine von IBM entwickelte Technologie, die in einer besonderen Ausformung und Programmierung für uns zusammengestellt wurde. Das Projekt hieß bei uns lange Zeit DocText Miner und heißt seit einiger Zeit auf Vorschlag von IBM DocCat, das soll eine Abkürzung für Document-Categoriser sein, sie ist ja auch nett und anschaulich. Wir fangen an mit Text-Mining, das bei IBM in Heidelberg entwickelt wurde. Die verstehen darunter das automatische Indexieren als eine Instanz, also einen Teil von Text-Mining. Probleme werden dabei gezeigt, und das Text-Mining ist eben eine Methode zur Strukturierung von und der Suche in großen Dokumentenmengen, die Extraktion von Informationen und, das ist der hohe Anspruch, von impliziten Zusammenhängen. Das letztere sei dahingestellt. IBM macht das quantitativ, empirisch, approximativ und schnell. das muss man wirklich sagen. Das Ziel, und das ist ganz wichtig für unser Projekt gewesen, ist nicht, den Text zu verstehen, sondern das Ergebnis dieser Verfahren ist, was sie auf Neudeutsch a bundle of words, a bag of words nennen, also eine Menge von bedeutungstragenden Begriffen aus einem Text zu extrahieren, aufgrund von Algorithmen, also im Wesentlichen aufgrund von Rechenoperationen. Es gibt eine ganze Menge von linguistischen Vorstudien, ein wenig Linguistik ist auch dabei, aber nicht die Grundlage der ganzen Geschichte. Was sie für uns gemacht haben, ist also die Annotierung von Pressetexten für unsere Pressedatenbank. Für diejenigen, die es noch nicht kennen: Gruner + Jahr führt eine Textdokumentation, die eine Datenbank führt, seit Anfang der 70er Jahre, da sind z.Z. etwa 6,5 Millionen Dokumente darin, davon etwas über 1 Million Volltexte ab 1993. Das Prinzip war lange Zeit, dass wir die Dokumente, die in der Datenbank gespeichert waren und sind, verschlagworten und dieses Prinzip haben wir auch dann, als der Volltext eingeführt wurde, in abgespeckter Form weitergeführt. Zu diesen 6,5 Millionen Dokumenten gehören dann eben auch ungefähr 10 Millionen Faksimileseiten, weil wir die Faksimiles auch noch standardmäßig aufheben.

Date

22. 4.2003 11:45:36

0.004109507 = product of:
  0.028766548 = sum of:
    0.028766548 = weight(_text_:of in 2338) [ClassicSimilarity], result of:
      0.028766548 = score(doc=2338,freq=24.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.41895083 = fieldWeight in 2338, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
  0.14285715 = coord(1/7)

Abstract

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2566-2579

0.0038831187 = product of:
  0.02718183 = sum of:
    0.02718183 = weight(_text_:of in 3603) [ClassicSimilarity], result of:
      0.02718183 = score(doc=3603,freq=42.0), product of:
        0.06866331 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.043909185 = queryNorm
        0.39587128 = fieldWeight in 3603, product of:
          6.4807405 = tf(freq=42.0), with freq of:
            42.0 = termFreq=42.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3603)
  0.14285715 = coord(1/7)

Abstract

This paper addresses a bibliometric methodology to discover the structure of the scientific 'landscape' in order to gain detailed insight into the development of MD fields, their interaction, and the transfer of knowledge between them. This methodology is appropriate to visualize the position of MD activities in relation to interdisciplinary MD developments, and particularly in relation to socio-economic problems. Furthermore, it allows the identification of the major actors. It even provides the possibility of foresight. We describe a first approach to apply bibliometric mapping as an instrument to investigate characteristics of knowledge transfer. In this paper we discuss the creation of 'maps of science' with help of advanced bibliometric methods. This 'bibliometric cartography' can be seen as a specific type of data-mining, applied to large amounts of scientific publications. As an example we describe the mapping of the field neuroscience, one of the largest and fast growing fields in the life sciences. The number of publications covered by this database is about 80,000 per year, the period covered is 1995-1998. Current research is going an to update the mapping for the years 1999-2002. This paper addresses the main lines of the methodology and its application in the study of knowledge transfer.

Source

Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase