Search (731 results, page 1 of 37)

  • × theme_ss:"Suchmaschinen"
  1. Bradley, P.: ¬The great search-engine con-trick (1999) 0.15 
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    e
    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  2. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.15 
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    e
    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  3. Green, D.: ¬The evolution of Web searching (1999) 0.15 
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    e
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    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  4. Martinez, A.M.; Sanchez, E.F.: Comparing Internet search tools (1999) 0.15 
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    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  5. Arnold, S.: Search engines 2000 : rough running, prone to stall (1999) 0.15 
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    e
    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
  6. Eymard, G.; Francony, J.-M.: Vers un serveur d'indexation et de recherche sur le WEB (1999) 0.13 
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    Series
    Collection travaux et recherches; UL3
    Source
    Organisation des connaissances en vue de leur intégration dans les systèmes de représentation et de recherche d'information. Ed.: J. Maniez, et al
  7. Pharo, N.: Web information search strategies : a model for classifying Web interaction (1999) 0.13 
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    Language
    e
    Source
    Vocabulary as a central concept in digital libraries: interdisciplinary concepts, challenges, and opportunities : proceedings of the Third International Conference an Conceptions of Library and Information Science (COLIS3), Dubrovnik, Croatia, 23-26 May 1999. Ed. by T. Arpanac et al
  8. Lewandowski, D.: Query understanding (2011) 0.11 
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    Abstract
    In diesem Kapitel wird beschrieben, wie Suchmaschinen Suchanfragen interpretieren können, um letztendlich den Nutzern besser auf ihren Kontext zugeschnittene Ergebnisse liefern zu können. Nach einer Diskussion der Notwendigkeit und der Einsatzmöglichkeiten des Query Understanding wird aufgezeigt, auf welcher Datenbasis und an welchen Ansatzpunkten Suchanfragen interpretiert werden können. Dann erfolgt eine Erläuterung der Interpretationsmöglichkeiten anhand der Suchanfragen-Facetten von Calderon-Benavides et al. (2010), welcher sich eine Diskussion der Verfahren zur Ermittlung der Facetten anschließt.
    Date
    18. 9.2018 18:22:18
  9. Brueggeman, P.: Full text Web indexes (1997) 0.10 
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    e
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    Tradition and innovation: planning our future. Proceedings of the 22nd Annual Conference of the International Association of Aquatic and Marine Science Libraries and Information Centres (IAMSLIC), Monterey Bay, CA, 13-18 Oct. 1996. Ed.: J.W. Markham et al
  10. Dasgupta, S.K.: Search engines : tooles for exploring the Internet (1998) 0.10 
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    e
    Source
    Information management in academic and research libraries: Proceedings of the 5th National convention for Automation of Libraries in Education and Research (CALIBER-98), Bhubaneswar, India, 4-5 March 1998. Ed. by M. Mahapatra et al
  11. Courtois, M.P.; Berry, M.W.: Results ranking in Web search engines (1999) 0.08 
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    Abstract
    Comparaison des méthodes de classement de 5 moteurs de recherche (AltaVista, HotBot, Excie, Infoseek et Lycos). Sont testées la présence de tous les mots, la proximité et la localisation
    Language
    e
  12. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.08 
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    e
    Source
    Dynamism and stability in knowledge organization: Proceedings of the 6th International ISKO-Conference, 10-13 July 2000, Toronto, Canada. Ed.: C. Beghtol et al
  13. Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001) 0.08 
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    Abstract
    The work we are presenting is devoted to the information collected on the WWW. By the term collected we mean the whole process of retrieving, extracting and presenting results to the user. This research is part of the RAP (Research, Analyze, Propose) project in which we propose to combine two methods: (i) query reformulation using linguistic markers according to a given point of view; and (ii) text semantic analysis by means of contextual exploration results (Descles, 1991). The general project architecture describing the interactions between the users, the RAP system and the WWW search engines is presented in Nait-Baha et al. (1998). We will focus this paper on showing how we use linguistic markers to reformulate the queries according to a given point of view
    Language
    e
  14. Van der Walt, M.: ¬The structure of classification schemes used in Internet search engines (1998) 0.06 
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    Language
    e
    Source
    Structures and relations in knowledge organization: Proceedings of the 5th International ISKO-Conference, Lille, 25.-29.8.1998. Ed.: W. Mustafa el Hadi et al
  15. Bilal, D.; Gwizdka, J.: Children's query types and reformulations in Google search (2018) 0.06 
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    Abstract
    We investigated the searching behaviors of twenty-four children in grades 6, 7, and 8 (ages 11-13) in finding information on three types of search tasks in Google. Children conducted 72 search sessions and issued 150 queries. Children's phrase- and question-like queries combined were much more prevalent than keyword queries (70% vs. 30%, respectively). Fifty two percent of the queries were reformulations (33 sessions). We classified children's query reformulation types into five classes based on the taxonomy by Liu et al. (2010). We found that most query reformulations were by Substitution and Specialization, and that children hardly repeated queries. We categorized children's queries by task facets and examined the way they expressed these facets in their query formulations and reformulations. Oldest children tended to target the general topic of search tasks in their queries most frequently, whereas younger children expressed one of the two facets more often. We assessed children's achieved task outcomes using the search task outcomes measure we developed. Children were mostly more successful on the fact-finding and fully self-generated task and partially successful on the research-oriented task. Query type, reformulation type, achieved task outcomes, and expressing task facets varied by task type and grade level. There was no significant effect of query length in words or of the number of queries issued on search task outcomes. The study findings have implications for human intervention, digital literacy, search task literacy, as well as for system intervention to support children's query formulation and reformulation during interaction with Google.
    Language
    e
  16. Krempl, S.: Google muss zerschlagen werden (2007) 0.06 
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    Content
    Vgl. die Studie "Maurer, H. et al: Report on dangers and opportunities posed by large search engines, particularly Google" unter: http://www.iicm.tugraz.at/iicm_papers/dangers_google.pdf.
  17. Dodge, M.: ¬A map of Yahoo! (2000) 0.04 
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    Content
    "Introduction Yahoo! is the undisputed king of the Web directories, providing one of the key information navigation tools on the Internet. It has maintained its popularity over many Internet-years as the most visited Web site, against intense competition. This is because it does a good job of shifting, cataloguing and organising the Web [1] . But what would a map of Yahoo!'s hierarchical classification of the Web look like? Would an interactive map of Yahoo!, rather than the conventional listing of sites, be more useful as navigational tool? We can get some idea what a map of Yahoo! might be like by taking a look at ET-Map, a prototype developed by Hsinchun Chen and colleagues in the Artificial Intelligence Lab [2] at the University of Arizona. ET-Map was developed in 1995 as part of innovative research in automatic Internet homepage categorization and it charts a large chunk of Yahoo!, from the entertainment section representing some 110,000 different Web links. The map is a two-dimensional, multi-layered category map; its aim is to provide an intuitive visual information browsing tool. ET-Map can be browsed interactively, explored and queried, using the familiar point-and-click navigation style of the Web to find information of interest.
    The View From Above Browsing for a particular piece on information on the Web can often feel like being stuck in an unfamiliar part of town walking around at street level looking for a particular store. You know the store is around there somewhere, but your viewpoint at ground level is constrained. What you really want is to get above the streets, hovering half a mile or so up in the air, to see the whole neighbourhood. This kind of birds-eye view function has been memorably described by David D. Clark, Senior Research Scientist at MIT's Laboratory for Computer Science and the Chairman of the Invisible Worlds Protocol Advisory Board, as the missing "up button" on the browser [3] . ET-Map is a nice example of a prototype for Clark's "up-button" view of an information space. The goal of information maps, like ET-Map, is to provide the browser with a sense of the lie of the information landscape, what is where, the location of clusters and hotspots, what is related to what. Ideally, this 'big-picture' all-in-one visual summary needs to fit on a single standard computer screen. ET-Map is one of my favourite examples, but there are many other interesting information maps being developed by other researchers and companies (see inset at the bottom of this page). How does ET-Map work? Here is a sequence of screenshots of a typical browsing session with ET-Map, which ends with access to Web pages on jazz musician Miles Davis. You can also tryout ET-Map for yourself, using a fully working demo on the AI Lab's website [4] . We begin with the top-level map showing forty odd broad entertainment 'subject regions' represented by regularly shaped tiles. Each tile is a visual summary of a group of Web pages with similar content. These tiles are shaded different colours to differentiate them, while labels identify the subject of the tile and the number in brackets telling you how many individual Web page links it contains. ET-Map uses two important, but common-sense, spatial concepts in its organisation and representation of the Web. Firstly, the 'subject regions' size is directly related to the number of Web pages in that category. For example, the 'MUSIC' subject area contains over 11,000 pages and so has a much larger area than the neighbouring area of 'LIVE' which only has 4,300 odd pages. This is intuitively meaningful, as the largest tiles are visually more prominent on the map and are likely to be more significant as they contain the most links. In addition, a second spatial concept, that of neighbourhood proximity, is applied so 'subject regions' closely related in term of content are plotted close to each other on the map. For example, 'FILM' and 'YEAR'S OSCARS', at the bottom left, are neighbours in both semantic and spatial space. This make senses as many things in the real-world are ordered in this way, with things that are alike being spatially close together (e.g. layout of goods in a store, or books in a library). Importantly, ET-Map is also a multi-layer map, with sub-maps showing greater informational resolution through a finer degree of categorization. So for any subject region that contains more than two hundred Web pages, a second-level map, with more detailed categories is generated. This subdivision of information space is repeated down the hierarchy as far as necessary. In the example, the user selected the 'MUSIC' subject region which, not surprisingly, contained many thousands of pages. A second-level map with numerous different music categories is then presented to the user. Delving deeper, the user wants to learn more about jazz music, so clicking on the 'JAZZ' tile leads to a third-level map, a fine-grained map of jazz related Web pages. Finally, selecting the 'MILES DAVIS' subject region leads to more a conventional looking ranking of pages from which the user selects one to download.
    ET-Map was created using a sophisticated AI technique called Kohonen self-organizing map, a neural network approach that has been used for automatic analysis and classification of semantic content of text documents like Web pages. I do not pretend to fully understand how this technique works; I tend to think of it as a clever 'black-box' that group together things that are alike [5] . It is a real challenge to automatically classify pages from a very heterogeneous information collection like the Web into categories that will match the conceptions of a typical user. Directories like Yahoo! tend to rely on the skill of human editors to achieve this. ET-Map is an interesting prototype that I think highlights well the potential for a map-based approach to Web browsing. I am surprised none of the major search engines or directories have introduced the option of mapping results. Although, I am sure many are working on ideas. People certainly need all the help they get, as Web growth shows no sign of slowing. Just last month it was reported that the Web had surpassed one billion indexable pages [6].
    Language
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  18. Agosti, M.; Pretto, L.: ¬A theoretical study of a generalized version of kleinberg's HITS algorithm (2005) 0.04 
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    Abstract
    Kleinberg's HITS (Hyperlink-Induced Topic Search) algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results.
  19. Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996) 0.03 
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    Date
    7. 2.1996 22:38:41
    Footnote
    e-mail: grossjoh@ls6.informatik.uni-dortmund.de
    Pages
    22 S
  20. Radev, D.R.; Libner, K.; Fan, W.: Getting answers to natural language questions on the Web (2002) 0.03 
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    Abstract
    Seven hundred natural language questions from TREC-8 and TREC-9 were sent by Radev, Libner, and Fan to each of nine web search engines. The top 40 sites returned by each system were stored for evaluation of their productivity of correct answers. Each question per engine was scored as the sum of the reciprocal ranks of identified correct answers. The large number of zero scores gave a positive skew violating the normality assumption for ANOVA, so values were transformed to zero for no hit and one for one or more hits. The non-zero values were then square-root transformed to remove the remaining positive skew. Interactions were observed between search engine and answer type (name, place, date, et cetera), search engine and number of proper nouns in the query, search engine and the need for time limitation, and search engine and total query words. All effects were significant. Shortest queries had the highest mean scores. One or more proper nouns present provides a significant advantage. Non-time dependent queries have an advantage. Place, name, person, and text description had mean scores between .85 and .9 with date at .81 and number at .59. There were significant differences in score by search engine. Search engines found at least one correct answer in between 87.7 and 75.45 of the cases. Google and Northern Light were just short of a 90% hit rate. No evidence indicated that a particular engine was better at answering any particular sort of question.
    Language
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Authors

Years

Languages

  • e 609
  • d 116
  • nl 5
  • f 3
  • sp 1
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Types

  • a 623
  • el 78
  • m 43
  • s 11
  • r 4
  • x 3
  • p 2
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Classifications