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  1. Thelwall, M.; Vaughan, L.; Björneborn, L.: Webometrics (2004) 0.01 
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    Theme
    Citation indexing
  2. Vaughan, L.; Shaw , D.: Bibliographic and Web citations : what Is the difference? (2003) 0.01 
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    Theme
    Citation indexing
  3. Eiriksson, J.M.; Retsloff, J.M.: Librarians in the 'information age' : promoter of change or provider of stability? (2005) 0.01 
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    Date
    22. 7.2009 11:23:22
  4. Hill, L.L.; Zheng, Q.: Indirect geospatial referencing through place names in the digital library : Alexandra digital library experience with developing and implementing gazetteers (1999) 0.01 
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    Date
    29. 9.2001 20:22:45
  5. Günther, S.: Aufbruch in den virtuellen Raum : Anleitung zum Aufbau eines Web Contact Centers (2005) 0.01 
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    Date
    22. 9.2005 20:50:31
  6. Rosenfeld, L.; Morville, P.: Information architecture for the World Wide Web : designing large-scale Web sites (2007) 0.01 
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    Date
    22. 3.2008 16:18:27
  7. Molholt, P.: Qualities of classification schemes for the Information Superhighway (1995) 0.01 
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    Source
    Cataloging and classification quarterly. 21(1995) no.2, S.19-22
  8. Electronic resources : selection and bibliograhic control (1996) 0.01 
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    Series
    Cataloging and classification quarterly; vol.22, nos.3/4
  9. Sundar, S.S.; Knobloch-Westerwick, S.; Hastall, M.R.: News cues : information scent and cognitive heuristics (2007) 0.01 
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    Date
    7. 3.2007 16:22:24
  10. Cox, A.M.: Flickr: a case study of Web2.0 (2008) 0.01 
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    Date
    30.12.2008 19:38:22
  11. Yang, C.C.; Liu, N.: Web site topic-hierarchy generation based on link structure (2009) 0.01 
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    Date
    22. 3.2009 12:51:47
  12. Hu, D.; Kaza, S.; Chen, H.: Identifying significant facilitators of dark network evolution (2009) 0.01 
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    Date
    22. 3.2009 18:50:30
  13. Kim, S.; Oh, S.: Users' relevance criteria for evaluating answers in a social Q&A site (2009) 0.01 
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    Date
    22. 3.2009 18:57:23
  14. Ku, L.-W.; Ho, H.-W.; Chen, H.-H.: Opinion mining and relationship discovery using CopeOpi opinion analysis system (2009) 0.01 
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    Abstract
    We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.
  15. Dufour, C.; Bartlett, J.C.; Toms, E.G.: Understanding how webcasts are used as sources of information (2011) 0.01 
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    Date
    22. 1.2011 14:16:14
  16. Bhattacharya, S.; Yang, C.; Srinivasan, P.; Boynton, B.: Perceptions of presidential candidates' personalities in twitter (2016) 0.01 
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    Date
    22. 1.2016 11:25:47
  17. Bhatia, S.; Biyani, P.; Mitra, P.: Identifying the role of individual user messages in an online discussion and its use in thread retrieval (2016) 0.01 
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    Date
    22. 1.2016 11:50:46
  18. Dalip, D.H.; Gonçalves, M.A.; Cristo, M.; Calado, P.: ¬A general multiview framework for assessing the quality of collaboratively created content on web 2.0 (2017) 0.01 
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    Date
    16.11.2017 13:04:22
  19. Chakrabarti, S.: Mining the Web : discovering knowledge from hypertext data (2003) 0.01 
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    Footnote
    Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.
  20. Priss, U.: Alternatives to the "Semantic Web" : multi-strategy knowledge representation (2003) 0.01 
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    Abstract
    This paper argues that the Semantic Web needs to incorporate both formal and associative structures (and possibly a multitude of other structures and strategies) to be successful. The arguments for this claim are based on an observation of successes and failures in the areas of artificial intelligence (AI) and natural language processing (NLP). 1. Introduction The WWW provides numerous challenges for information and knowledge processing activities. Information may be available but not accessible or retrievable because of language barriers or insufficient search strategies. Data mining techniques may discover implicit information in explicit data but these techniques do not necessarily guarantee that the discovered information is relevant, significant and trustworthy. During the last several decades hundreds or thousands of computer and information scientists have developed probably thousands of natural language processing and artificial intelligence techniques that were aimed at solving problems related to intelligent information processing only to encounter more and more new obstacles along the way. The latest solution, the Semantic Web, appears as an open declaration of defeat: since natural language processing and AI techniques did not provide sufficient results, it is now proposed to put the burden an the shoulder of the authors of webpages who are expected to populate their pages with metadata and additional markup. Metadata is essentially a new form of controlled vocabulary; markup - at least in the form of XML, XSL, etc - is essentially a programming language. Existing studies of the use of controlled vocabularies and indexing practices in information science and studies of teaching programming languages to "everybody" (Python, 2002) have shown that both are difficult and full of unsolved problems. This can further dampen the expectations of the success of the Semantic Web. In contrast to machines and despite numerous inter-cultural conflicts around the world, humans do communicate surprisingly successfully even across national, linguistic and cultural boundaries. The question then arises: why are humans successful at information processing tasks such as information integration, translation and communication, which computers find so difficult? One obvious answer is that human cognition is embodied and grounded in our shared experiences of living in the same world. AI researchers have theoretically explored the idea of symbol grounding in the early 1990's but so far, connectionist artificial agents with perceptual interfaces have not been integrated with a large-scale capability of symbolic representations.

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