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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.23 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.19 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  3. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.19 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  4. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.17 
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  5. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.14 
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  6. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.14 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  7. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.14 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  8. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.12 
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  9. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.11 
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    Date
    25. 8.2005 17:42:22
  10. Dunning, A.: Do we still need search engines? (1999) 0.11 
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    Source
    Ariadne. 1999, no.22
  11. Hsieh-Yee, I.: ¬The retrieval power of selected search engines : how well do they address general reference questions and subject questions? (1998) 0.11 
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    Abstract
    Evaluates the performance of 8 major Internet search engines in answering 21 real reference questions and 5 made up subject questions. Reports on the retrieval and relevancy ranking abilities of the search engines. Concludes that the search engines did not produce good results for the reference questions unlike for the subject questions. The best engines are identified by type of questions, with Infoseek best for the subject questions, and OpenText best for refrence questions
    Date
    25.12.1998 19:22:51
  12. Su, L.T.: ¬A comprehensive and systematic model of user evaluation of Web search engines : Il. An evaluation by undergraduates (2003) 0.11 
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    Abstract
    This paper presents an application of the model described in Part I to the evaluation of Web search engines by undergraduates. The study observed how 36 undergraduate used four major search engines to find information for their own individual problems and how they evaluated these engines based an actual interaction with the search engines. User evaluation was based an 16 performance measures representing five evaluation criteria: relevance, efficiency, utility, user satisfaction, and connectivity. Non-performance (user-related) measures were also applied. Each participant searched his/ her own topic an all four engines and provided satisfaction ratings for system features and interaction and reasons for satisfaction. Each also made relevance judgements of retrieved items in relation to his/her own information need and participated in post-search Interviews to provide reactions to the search results and overall performance. The study found significant differences in precision PR1 relative recall, user satisfaction with output display, time saving, value of search results, and overall performance among the four engines and also significant engine by discipline interactions an all these measures. In addition, the study found significant differences in user satisfaction with response time among four engines, and significant engine by discipline interaction in user satisfaction with search interface. None of the four search engines dominated in every aspect of the multidimensional evaluation. Content analysis of verbal data identified a number of user criteria and users evaluative comments based an these criteria. Results from both quantitative analysis and content analysis provide insight for system design and development, and useful feedback an strengths and weaknesses of search engines for system improvement
    Date
    24. 1.2004 18:27:22
  13. Conhaim, W.W.: Search tools (1996) 0.10 
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    Abstract
    Describes the 3 most popular searching tools for the WWW: InfoSeek, Yahoo and Lycos. Searching Internet directories can also be a useful search technique. Lists other searching engines. Points out a number of evaluations of these search engines published on the WWW. A number of search tools are available for specialized areas. Sites are available that enable parallel searching using several tools at once. Describes WWW pages with information about search engines
    Date
    1. 8.1996 22:39:31
  14. Notess, G.R.: Toward more comprehensive Web searching : single searching versus megasearching (1998) 0.10 
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    Abstract
    In spite of their size, the major Web indexes are not comprehensive. Considers approaches carrying out comprehensive searches, highlighting their advantages and disadvantages. In the single search tool approach, users search the largest of the databses one by one, using the command language uniqe to each to increase the precision of the esearch. In the megasearch approach, search engines use 1 form that simultaneously seands a single query to a number of search engines and then presents the results. Inference Find, Dogpile and MetaFind are examples of good metasearch engines
    Source
    Online. 22(1998) no.2, S.73-76
  15. Berinstein, P.: Turning visual : image search engines on the Web (1998) 0.10 
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    Abstract
    Gives an overview of image search engines on the Web. They work by: looking for graphics files; looking for a caption; looking for Web sites whose titles indicate the presence of picturres on a certain subject; or employing human intervention. Describes the image search capabilities of: AltaVista; Amazing Picture Machine (Http://www.ncrtec.org/picture.htm); HotBot; ImageSurfer (http://ipix.yahoo.com); Lycos; Web Clip Art Search Engine and WebSEEK. The search engines employing human intervention provide the best results
    Source
    Online. 22(1998) no.3, S.37-38,40-42
  16. Marchiori, M.: ¬The quest for correct information on the Web : hyper search engines (1997) 0.10 
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    Abstract
    Presents a novel method to extract from a web object its hyper informative content, in contrast with current search engines, which only deal with the textual information content. This method is not only valuable per se, but it is shown to be able to considerably increase the precision of current search engines. It integrates with existing search engine technology since it can be implemented on top of every search engine, acting as a post-processor, thus automatically transforming a search engine into its corresponding hyper version. Shows how the hyper information can be usefully employed to face the search engines persuasion problem
    Date
    1. 8.1996 22:08:06
  17. Fan, W.; Gordon, M.D.; Pathak, P.: ¬A generic ranking function discovery framework by genetic programming for information retrieval (2004) 0.10 
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    Abstract
    Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
  18. Adorni, G.; Poggi, A.: Object-oriented programming languages : a natural framework for distributed artificial intelligence (1997) 0.10 
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    Source
    Encyclopedia of library and information science. Vol.59, [=Suppl.22]
  19. Carroll, N.: Search engine optimization (2009) 0.09 
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    Abstract
    Search engine optimization (SEO) is the craft of elevating Web sites or individual Web site pages to higher rankings on search engines through programming, marketing, or content acumen. This section covers the origins of SEO, strategies and tactics, history and trends, and the evolution of user behavior in online searching.
  20. Thelwall, M.: Quantitative comparisons of search engine results (2008) 0.09 
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    Abstract
    Search engines are normally used to find information or Web sites, but Webometric investigations use them for quantitative data such as the number of pages matching a query and the international spread of those pages. For this type of application, the accuracy of the hit count estimates and range of URLs in the full results are important. Here, we compare the applications programming interfaces of Google, Yahoo!, and Live Search for 1,587 single word searches. The hit count estimates were broadly consistent but with Yahoo! and Google, reporting 5-6 times more hits than Live Search. Yahoo! tended to return slightly more matching URLs than Google, with Live Search returning significantly fewer. Yahoo!'s result URLs included a significantly wider range of domains and sites than the other two, and there was little consistency between the three engines in the number of different domains. In contrast, the three engines were reasonably consistent in the number of different top-level domains represented in the result URLs, although Yahoo! tended to return the most. In conclusion, quantitative results from the three search engines are mostly consistent but with unexpected types of inconsistency that users should be aware of. Google is recommended for hit count estimates but Yahoo! is recommended for all other Webometric purposes.

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