Search (113 results, page 1 of 6)

  • × theme_ss:"Retrievalalgorithmen"
  1. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.13 
    0.1318309 = product of:
  0.19774634 = sum of:
    0.067437425 = weight(_text_:wide in 1319) [ClassicSimilarity], result of:
      0.067437425 = score(doc=1319,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.342674 = fieldWeight in 1319, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1319)
    0.063368805 = weight(_text_:web in 1319) [ClassicSimilarity], result of:
      0.063368805 = score(doc=1319,freq=6.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.43716836 = fieldWeight in 1319, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1319)
    0.04587784 = weight(_text_:computer in 1319) [ClassicSimilarity], result of:
      0.04587784 = score(doc=1319,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = 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)
    0.021062255 = product of:
      0.04212451 = sum of:
        0.04212451 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
          0.04212451 = score(doc=1319,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = 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)
  0.6666667 = coord(4/6)
    Abstract
    Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia
    Source
    Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
  2. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.11 
    0.10628815 = product of:
  0.2125763 = sum of:
    0.0817465 = weight(_text_:wide in 5777) [ClassicSimilarity], result of:
      0.0817465 = score(doc=5777,freq=4.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.4153836 = fieldWeight in 5777, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
    0.062718846 = weight(_text_:web in 5777) [ClassicSimilarity], result of:
      0.062718846 = score(doc=5777,freq=8.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.43268442 = fieldWeight in 5777, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
    0.068110935 = weight(_text_:computer in 5777) [ClassicSimilarity], result of:
      0.068110935 = score(doc=5777,freq=6.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.41961014 = fieldWeight in 5777, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
  0.5 = coord(3/6)
    Abstract
    This book discusses many of the key design issues for building search engines and emphazises the important role that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms, and software but also user-centered issues such as interfaces, manual indexing, and document preparation. They also present some of the current problems in information retrieval that many not be familiar to applied mathematicians and computer scientists and some of the driving computational methods (SVD, SDD) for automated conceptual indexing
    LCSH
    Web search engines
    Text processing (Computer science)
    RSWK
    World Wide Web / Suchmaschine / Mathematisches Modell (BVB)
    Subject
    World Wide Web / Suchmaschine / Mathematisches Modell (BVB)
    Web search engines
    Text processing (Computer science)
  3. Austin, D.: How Google finds your needle in the Web's haystack : as we'll see, the trick is to ask the web itself to rank the importance of pages... (2006) 0.09 
    0.09150508 = product of:
  0.13725762 = sum of:
    0.033718713 = weight(_text_:wide in 93) [ClassicSimilarity], result of:
      0.033718713 = score(doc=93,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.171337 = fieldWeight in 93, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.02734375 = fieldNorm(doc=93)
    0.05174041 = weight(_text_:web in 93) [ClassicSimilarity], result of:
      0.05174041 = score(doc=93,freq=16.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.35694647 = fieldWeight in 93, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.02734375 = fieldNorm(doc=93)
    0.02293892 = weight(_text_:computer in 93) [ClassicSimilarity], result of:
      0.02293892 = score(doc=93,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.14131951 = fieldWeight in 93, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.02734375 = fieldNorm(doc=93)
    0.028859572 = product of:
      0.057719145 = sum of:
        0.057719145 = weight(_text_:programs in 93) [ClassicSimilarity], result of:
          0.057719145 = score(doc=93,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.22416902 = fieldWeight in 93, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.02734375 = fieldNorm(doc=93)
      0.5 = coord(1/2)
  0.6666667 = coord(4/6)
    Abstract
    Imagine a library containing 25 billion documents but with no centralized organization and no librarians. In addition, anyone may add a document at any time without telling anyone. You may feel sure that one of the documents contained in the collection has a piece of information that is vitally important to you, and, being impatient like most of us, you'd like to find it in a matter of seconds. How would you go about doing it? Posed in this way, the problem seems impossible. Yet this description is not too different from the World Wide Web, a huge, highly-disorganized collection of documents in many different formats. Of course, we're all familiar with search engines (perhaps you found this article using one) so we know that there is a solution. This article will describe Google's PageRank algorithm and how it returns pages from the web's collection of 25 billion documents that match search criteria so well that "google" has become a widely used verb. Most search engines, including Google, continually run an army of computer programs that retrieve pages from the web, index the words in each document, and store this information in an efficient format. Each time a user asks for a web search using a search phrase, such as "search engine," the search engine determines all the pages on the web that contains the words in the search phrase. (Perhaps additional information such as the distance between the words "search" and "engine" will be noted as well.) Here is the problem: Google now claims to index 25 billion pages. Roughly 95% of the text in web pages is composed from a mere 10,000 words. This means that, for most searches, there will be a huge number of pages containing the words in the search phrase. What is needed is a means of ranking the importance of the pages that fit the search criteria so that the pages can be sorted with the most important pages at the top of the list. One way to determine the importance of pages is to use a human-generated ranking. For instance, you may have seen pages that consist mainly of a large number of links to other resources in a particular area of interest. Assuming the person maintaining this page is reliable, the pages referenced are likely to be useful. Of course, the list may quickly fall out of date, and the person maintaining the list may miss some important pages, either unintentionally or as a result of an unstated bias. Google's PageRank algorithm assesses the importance of web pages without human evaluation of the content. In fact, Google feels that the value of its service is largely in its ability to provide unbiased results to search queries; Google claims, "the heart of our software is PageRank." As we'll see, the trick is to ask the web itself to rank the importance of pages.
  4. Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000) 0.05 
    0.054975893 = product of:
  0.16492768 = sum of:
    0.073171996 = weight(_text_:web in 3662) [ClassicSimilarity], result of:
      0.073171996 = score(doc=3662,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.50479853 = fieldWeight in 3662, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.109375 = fieldNorm(doc=3662)
    0.09175568 = weight(_text_:computer in 3662) [ClassicSimilarity], result of:
      0.09175568 = score(doc=3662,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.56527805 = fieldWeight in 3662, 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=3662)
  0.33333334 = coord(2/6)
    Source
    Computer networks. 33(2000) nos.1/6, S.449-455
  5. Finding anything in the billion page Web : are algorithms the key? (1999) 0.05 
    0.054975893 = product of:
  0.16492768 = sum of:
    0.073171996 = weight(_text_:web in 6248) [ClassicSimilarity], result of:
      0.073171996 = score(doc=6248,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.50479853 = fieldWeight in 6248, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.109375 = fieldNorm(doc=6248)
    0.09175568 = weight(_text_:computer in 6248) [ClassicSimilarity], result of:
      0.09175568 = score(doc=6248,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.56527805 = fieldWeight in 6248, 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=6248)
  0.33333334 = coord(2/6)
    Source
    Computer networks. 31(1999) no.11-16, S.1760-1761
  6. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.04 
    0.044626735 = product of:
  0.1338802 = sum of:
    0.09175568 = weight(_text_:computer in 2134) [ClassicSimilarity], result of:
      0.09175568 = score(doc=2134,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = 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)
    0.04212451 = product of:
      0.08424902 = sum of:
        0.08424902 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
          0.08424902 = score(doc=2134,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = 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
  7. Chang, R.: Keyword searching and indexing (1993) 0.04 
    0.03946552 = product of:
  0.11839656 = sum of:
    0.05243182 = weight(_text_:computer in 7223) [ClassicSimilarity], result of:
      0.05243182 = score(doc=7223,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 7223, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=7223)
    0.065964736 = product of:
      0.13192947 = sum of:
        0.13192947 = weight(_text_:programs in 7223) [ClassicSimilarity], result of:
          0.13192947 = score(doc=7223,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.5123863 = fieldWeight in 7223, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.0625 = fieldNorm(doc=7223)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)
    Abstract
    Explains how a computer indexing system works. Reviews fundamentals of how data are stored and retrieved by computers. Describes B-Tree and B+-Tree indexing structures. Gives basic keyword searching techniques that the user must apply to make use of the indexing programs. The demand for keyword retrieval is increasing and librarians should expect to see the keyword-indexing feature become commonly available
  8. Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001) 0.04 
    0.03737321 = product of:
  0.11211963 = sum of:
    0.057803504 = weight(_text_:wide in 5771) [ClassicSimilarity], result of:
      0.057803504 = score(doc=5771,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 5771, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=5771)
    0.054316122 = weight(_text_:web in 5771) [ClassicSimilarity], result of:
      0.054316122 = score(doc=5771,freq=6.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.37471575 = fieldWeight in 5771, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=5771)
  0.33333334 = coord(2/6)
    Abstract
    In unserem Retrievaltest von Suchwerkzeugen im World Wide Web (Password 11/2000) schnitt die Suchmaschine Google am besten ab. Im Vergleich zu anderen Search Engines setzt Google kaum auf Informationslinguistik, sondern auf Algorithmen, die sich aus den Besonderheiten der Web-Dokumente ableiten lassen. Kernstück der informationsstatistischen Technik ist das "PageRank"- Verfahren (benannt nach dem Entwickler Larry Page), das aus der Hypertextstruktur des Web die "Popularität" von Seiten anhand ihrer ein- und ausgehenden Links berechnet. Google besticht durch das Angebot intuitiv verstehbarer Suchbildschirme sowie durch einige sehr nützliche "Kleinigkeiten" wie die Angabe des Rangs einer Seite, Highlighting, Suchen in der Seite, Suchen innerhalb eines Suchergebnisses usw., alles verstaut in einer eigenen Befehlsleiste innerhalb des Browsers. Ähnlich wie RealNames bietet Google mit dem Produkt "AdWords" den Aufkauf von Suchtermen an. Nach einer Reihe von nunmehr vier Password-Artikeln über InternetSuchwerkzeugen im Vergleich wollen wir abschließend zu einer Bewertung kommen. Wie ist der Stand der Technik bei Directories und Search Engines aus informationswissenschaftlicher Sicht einzuschätzen? Werden die "typischen" Internetnutzer, die ja in der Regel keine Information Professionals sind, adäquat bedient? Und können auch Informationsfachleute von den Suchwerkzeugen profitieren?
  9. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.03 
    0.034050807 = product of:
  0.10215242 = sum of:
    0.057803504 = weight(_text_:wide in 105) [ClassicSimilarity], result of:
      0.057803504 = score(doc=105,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 105, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.04434892 = weight(_text_:web in 105) [ClassicSimilarity], result of:
      0.04434892 = score(doc=105,freq=4.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.3059541 = fieldWeight in 105, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
  0.33333334 = coord(2/6)
    Abstract
    The rapid development of the Internet and World Wide Web has caused some critical problem for information retrieval. Researchers have made several attempts to solve these problems. Thesauri and subject heading lists as traditional information retrieval tools have been criticised for their efficiency to tackle these newly emerging problems. This paper proposes an information retrieval tool generated by cocitation analysis, comprising keyword clusters with relationships based on the co-occurrences of keywords in the literature. Such a tool can play the role of an associative thesaurus that can provide information about the keywords in a domain that might be useful for information searching and query expansion
  10. Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012) 0.03 
    0.034050807 = product of:
  0.10215242 = sum of:
    0.057803504 = weight(_text_:wide in 101) [ClassicSimilarity], result of:
      0.057803504 = score(doc=101,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 101, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=101)
    0.04434892 = weight(_text_:web in 101) [ClassicSimilarity], result of:
      0.04434892 = score(doc=101,freq=4.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.3059541 = fieldWeight in 101, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=101)
  0.33333334 = coord(2/6)
    Abstract
    Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.
  11. Henzinger, M.R.: Hyperlink analysis for the Web (2001) 0.03 
    0.033864863 = product of:
  0.10159458 = sum of:
    0.07537867 = weight(_text_:web in 8) [ClassicSimilarity], result of:
      0.07537867 = score(doc=8,freq=26.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.520022 = fieldWeight in 8, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.03125 = fieldNorm(doc=8)
    0.02621591 = weight(_text_:computer in 8) [ClassicSimilarity], result of:
      0.02621591 = score(doc=8,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.16150802 = fieldWeight in 8, 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=8)
  0.33333334 = coord(2/6)
    Abstract
    Hyperlink analysis algorithms allow search engines to deliver focused results to user queries.This article surveys ranking algorithms used to retrieve information on the Web.
    Content
    Information retrieval is a computer science subfield whose goal is to find all documents relevant to a user query in a given collection of documents. As such, information retrieval should really be called document retrieval. Before the advent of the Web, IR systems were typically installed in libraries for use mostly by reference librarians. The retrieval algorithm for these systems was usually based exclusively on analysis of the words in the document. The Web changed all this. Now each Web user has access to various search engines whose retrieval algorithms often use not only the words in the documents but also information like the hyperlink structure of the Web or markup language tags. How are hyperlinks useful? The hyperlink functionality alone-that is, the hyperlink to Web page B that is contained in Web page A-is not directly useful in information retrieval. However, the way Web page authors use hyperlinks can give them valuable information content. Authors usually create hyperlinks they think will be useful to readers. Some may be navigational aids that, for example, take the reader back to the site's home page; others provide access to documents that augment the content of the current page. The latter tend to point to highquality pages that might be on the same topic as the page containing the hyperlink. Web information retrieval systems can exploit this information to refine searches for relevant documents. Hyperlink analysis significantly improves the relevance of the search results, so much so that all major Web search engines claim to use some type of hyperlink analysis. However, the search engines do not disclose details about the type of hyperlink analysis they perform- mostly to avoid manipulation of search results by Web-positioning companies. In this article, I discuss how hyperlink analysis can be applied to ranking algorithms, and survey other ways Web search engines can use this analysis.
  12. Lalmas, M.: XML retrieval (2009) 0.03 
    0.031504404 = product of:
  0.09451321 = sum of:
    0.04816959 = weight(_text_:wide in 4998) [ClassicSimilarity], result of:
      0.04816959 = score(doc=4998,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 4998, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4998)
    0.04634362 = weight(_text_:computer in 4998) [ClassicSimilarity], result of:
      0.04634362 = score(doc=4998,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.28550854 = fieldWeight in 4998, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4998)
  0.33333334 = coord(2/6)
    Abstract
    Documents usually have a content and a structure. The content refers to the text of the document, whereas the structure refers to how a document is logically organized. An increasingly common way to encode the structure is through the use of a mark-up language. Nowadays, the most widely used mark-up language for representing structure is the eXtensible Mark-up Language (XML). XML can be used to provide a focused access to documents, i.e. returning XML elements, such as sections and paragraphs, instead of whole documents in response to a query. Such focused strategies are of particular benefit for information repositories containing long documents, or documents covering a wide variety of topics, where users are directed to the most relevant content within a document. The increased adoption of XML to represent a document structure requires the development of tools to effectively access documents marked-up in XML. This book provides a detailed description of query languages, indexing strategies, ranking algorithms, presentation scenarios developed to access XML documents. Major advances in XML retrieval were seen from 2002 as a result of INEX, the Initiative for Evaluation of XML Retrieval. INEX, also described in this book, provided test sets for evaluating XML retrieval effectiveness. Many of the developments and results described in this book were investigated within INEX.
    LCSH
    Query languages (Computer science)
    Subject
    Query languages (Computer science)
  13. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment (1998) 0.03 
    0.029720977 = product of:
  0.08916293 = sum of:
    0.057803504 = weight(_text_:wide in 5) [ClassicSimilarity], result of:
      0.057803504 = score(doc=5,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 5, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=5)
    0.031359423 = weight(_text_:web in 5) [ClassicSimilarity], result of:
      0.031359423 = score(doc=5,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.21634221 = fieldWeight in 5, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=5)
  0.33333334 = coord(2/6)
    Abstract
    The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of "authoritative" information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of "hub pages" that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.
  14. Picard, J.; Savoy, J.: Enhancing retrieval with hyperlinks : a general model based on propositional argumentation systems (2003) 0.03 
    0.028375676 = product of:
  0.085127026 = sum of:
    0.04816959 = weight(_text_:wide in 1427) [ClassicSimilarity], result of:
      0.04816959 = score(doc=1427,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 1427, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1427)
    0.036957435 = weight(_text_:web in 1427) [ClassicSimilarity], result of:
      0.036957435 = score(doc=1427,freq=4.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.25496176 = fieldWeight in 1427, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1427)
  0.33333334 = coord(2/6)
    Abstract
    Fast, effective, and adaptable techniques are needed to automatically organize and retrieve information an the ever-increasing World Wide Web. In that respect, different strategies have been suggested to take hypertext links into account. For example, hyperlinks have been used to (1) enhance document representation, (2) improve document ranking by propagating document score, (3) provide an indicator of popularity, and (4) find hubs and authorities for a given topic. Although the TREC experiments have not demonstrated the usefulness of hyperlinks for retrieval, the hypertext structure is nevertheless an essential aspect of the Web, and as such, should not be ignored. The development of abstract models of the IR task was a key factor to the improvement of search engines. However, at this time conceptual tools for modeling the hypertext retrieval task are lacking, making it difficult to compare, improve, and reason an the existing techniques. This article proposes a general model for using hyperlinks based an Probabilistic Argumentation Systems, in which each of the above-mentioned techniques can be stated. This model will allow to discover some inconsistencies in the mentioned techniques, and to take a higher level and systematic approach for using hyperlinks for retrieval.
  15. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.03 
    0.02692407 = product of:
  0.080772206 = sum of:
    0.062718846 = weight(_text_:web in 2239) [ClassicSimilarity], result of:
      0.062718846 = score(doc=2239,freq=8.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.43268442 = fieldWeight in 2239, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2239)
    0.01805336 = product of:
      0.03610672 = sum of:
        0.03610672 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
          0.03610672 = score(doc=2239,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.23214069 = fieldWeight in 2239, 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=2239)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)
    Abstract
    Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
    Date
    31. 5.2004 19:22:06
  16. Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.03 
    0.026011107 = product of:
  0.07803332 = sum of:
    0.045263432 = weight(_text_:web in 2696) [ClassicSimilarity], result of:
      0.045263432 = score(doc=2696,freq=6.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.3122631 = fieldWeight in 2696, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
    0.03276989 = weight(_text_:computer in 2696) [ClassicSimilarity], result of:
      0.03276989 = score(doc=2696,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.20188503 = fieldWeight in 2696, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
  0.33333334 = coord(2/6)
    Abstract
    Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
    Source
    International journal of computer applications. 129(2015) no.5, S12-15
  17. Kantor, P.; Kim, M.H.; Ibraev, U.; Atasoy, K.: Estimating the number of relevant documents in enormous collections (1999) 0.02 
    0.02476748 = product of:
  0.07430244 = sum of:
    0.04816959 = weight(_text_:wide in 6690) [ClassicSimilarity], result of:
      0.04816959 = score(doc=6690,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 6690, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6690)
    0.026132854 = weight(_text_:web in 6690) [ClassicSimilarity], result of:
      0.026132854 = score(doc=6690,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.18028519 = fieldWeight in 6690, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6690)
  0.33333334 = coord(2/6)
    Abstract
    In assessing information retrieval systems, it is important to know not only the precision of the retrieved set, but also to compare the number of retrieved relevant items to the total number of relevant items. For large collections, such as the TREC test collections, or the World Wide Web, it is not possible to enumerate the entire set of relevant documents. If the retrieved documents are evaluated, a variant of the statistical "capture-recapture" method can be used to estimate the total number of relevant documents, providing the several retrieval systems used are sufficiently independent. We show that the underlying signal detection model supporting such an analysis can be extended in two ways. First, assuming that there are two distinct performance characteristics (corresponding to the chance of retrieving a relevant, and retrieving a given non-relevant document), we show that if there are three or more independent systems available it is possible to estimate the number of relevant documents without actually having to decide whether each individual document is relevant. We report applications of this 3-system method to the TREC data, leading to the conclusion that the independence assumptions are not satisfied. We then extend the model to a multi-system, multi-problem model, and show that it is possible to include statistical dependencies of all orders in the model, and determine the number of relevant documents for each of the problems in the set. Application to the TREC setting will be presented
  18. Symonds, M.; Bruza, P.; Zuccon, G.; Koopman, B.; Sitbon, L.; Turner, I.: Automatic query expansion : a structural linguistic perspective (2014) 0.02 
    0.02476748 = product of:
  0.07430244 = sum of:
    0.04816959 = weight(_text_:wide in 1338) [ClassicSimilarity], result of:
      0.04816959 = score(doc=1338,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 1338, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1338)
    0.026132854 = weight(_text_:web in 1338) [ClassicSimilarity], result of:
      0.026132854 = score(doc=1338,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.18028519 = fieldWeight in 1338, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1338)
  0.33333334 = coord(2/6)
    Abstract
    A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.
  19. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.02 
    0.023934526 = product of:
  0.07180358 = sum of:
    0.05675911 = weight(_text_:computer in 1753) [ClassicSimilarity], result of:
      0.05675911 = score(doc=1753,freq=6.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.34967512 = fieldWeight in 1753, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1753)
    0.0150444675 = product of:
      0.030088935 = sum of:
        0.030088935 = weight(_text_:22 in 1753) [ClassicSimilarity], result of:
          0.030088935 = score(doc=1753,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.19345059 = fieldWeight in 1753, 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=1753)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)
    Abstract
    This book offers a comprehensive and consistent mathematical approach to information retrieval (IR) without which no implementation is possible, and sheds an entirely new light upon the structure of IR models. It contains the descriptions of all IR models in a unified formal style and language, along with examples for each, thus offering a comprehensive overview of them. The book also creates mathematical foundations and a consistent mathematical theory (including all mathematical results achieved so far) of IR as a stand-alone mathematical discipline, which thus can be read and taught independently. Also, the book contains all necessary mathematical knowledge on which IR relies, to help the reader avoid searching different sources. The book will be of interest to computer or information scientists, librarians, mathematicians, undergraduate students and researchers whose work involves information retrieval.
    Date
    22. 3.2008 12:26:32
    LCSH
    Computer science / Mathematics
    Subject
    Computer science / Mathematics
  20. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.02 
    0.022213614 = product of:
  0.06664084 = sum of:
    0.029565949 = weight(_text_:web in 7) [ClassicSimilarity], result of:
      0.029565949 = score(doc=7,freq=4.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.2039694 = fieldWeight in 7, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.03125 = fieldNorm(doc=7)
    0.037074894 = weight(_text_:computer in 7) [ClassicSimilarity], result of:
      0.037074894 = score(doc=7,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.22840683 = fieldWeight in 7, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.03125 = fieldNorm(doc=7)
  0.33333334 = coord(2/6)
    LCSH
    Web search engines
    Text processing (Computer science)
    Subject
    Web search engines
    Text processing (Computer science)

Authors

Years

Languages

  • e 101
  • d 11
  • m 1
  • More… Less…

Types

  • a 100
  • m 8
  • el 4
  • s 3
  • r 1
  • x 1
  • More… Less…

Themes

Subjects

Classifications