Search (27 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  • × theme_ss:"Suchmaschinen"
  1. Wills, R.S.: Google's PageRank : the math behind the search engine (2006) 0.05 
    0.048239887 = product of:
  0.1286397 = sum of:
    0.03422346 = weight(_text_:use in 5954) [ClassicSimilarity], result of:
      0.03422346 = score(doc=5954,freq=8.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.27065295 = fieldWeight in 5954, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.03125 = fieldNorm(doc=5954)
    0.01728394 = weight(_text_:of in 5954) [ClassicSimilarity], result of:
      0.01728394 = score(doc=5954,freq=30.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.26765788 = fieldWeight in 5954, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=5954)
    0.0771323 = sum of:
      0.019740483 = weight(_text_:on in 5954) [ClassicSimilarity], result of:
        0.019740483 = score(doc=5954,freq=10.0), product of:
          0.090823986 = queryWeight, product of:
            2.199415 = idf(docFreq=13325, maxDocs=44218)
            0.041294612 = queryNorm
          0.21734878 = fieldWeight in 5954, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            2.199415 = idf(docFreq=13325, maxDocs=44218)
            0.03125 = fieldNorm(doc=5954)
      0.05739182 = weight(_text_:line in 5954) [ClassicSimilarity], result of:
        0.05739182 = score(doc=5954,freq=2.0), product of:
          0.23157367 = queryWeight, product of:
            5.6078424 = idf(docFreq=440, maxDocs=44218)
            0.041294612 = queryNorm
          0.24783395 = fieldWeight in 5954, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.6078424 = idf(docFreq=440, maxDocs=44218)
            0.03125 = fieldNorm(doc=5954)
  0.375 = coord(3/8)
    Abstract
    Approximately 91 million American adults use the Internet on a typical day The number-one Internet activity is reading and writing e-mail. Search engine use is next in line and continues to increase in popularity. In fact, survey findings indicate that nearly 60 million American adults use search engines on a given day. Even though there are many Internet search engines, Google, Yahoo!, and MSN receive over 81% of all search requests. Despite claims that the quality of search provided by Yahoo! and MSN now equals that of Google, Google continues to thrive as the search engine of choice, receiving over 46% of all search requests, nearly double the volume of Yahoo! and over four times that of MSN. I use Google's search engine on a daily basis and rarely request information from other search engines. One day, I decided to visit the homepages of Google. Yahoo!, and MSN to compare the quality of search results. Coffee was on my mind that day, so I entered the simple query "coffee" in the search box at each homepage. Table 1 shows the top ten (unsponsored) results returned by each search engine. Although ordered differently, two webpages, www.peets.com and www.coffeegeek.com, appear in all three top ten lists. In addition, each pairing of top ten lists has two additional results in common. Depending on the information I hoped to obtain about coffee by using the search engines, I could argue that any one of the three returned better results: however, I was not looking for a particular webpage, so all three listings of search results seemed of equal quality. Thus, I plan to continue using Google. My decision is indicative of the problem Yahoo!, MSN, and other search engine companies face in the quest to obtain a larger percentage of Internet search volume. Search engine users are loyal to one or a few search engines and are generally happy with search results. Thus, as long as Google continues to provide results deemed high in quality, Google likely will remain the top search engine. But what set Google apart from its competitors in the first place? The answer is PageRank. In this article I explain this simple mathematical algorithm that revolutionized Web search.
  2. Thelwall, M.: Can Google's PageRank be used to find the most important academic Web pages? (2003) 0.04 
    0.036866955 = product of:
  0.07373391 = sum of:
    0.025048172 = weight(_text_:retrieval in 4457) [ClassicSimilarity], result of:
      0.025048172 = score(doc=4457,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 4457, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4457)
    0.025667597 = weight(_text_:use in 4457) [ClassicSimilarity], result of:
      0.025667597 = score(doc=4457,freq=2.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.20298971 = fieldWeight in 4457, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.046875 = fieldNorm(doc=4457)
    0.016396983 = weight(_text_:of in 4457) [ClassicSimilarity], result of:
      0.016396983 = score(doc=4457,freq=12.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.25392252 = fieldWeight in 4457, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4457)
    0.006621159 = product of:
      0.013242318 = sum of:
        0.013242318 = weight(_text_:on in 4457) [ClassicSimilarity], result of:
          0.013242318 = score(doc=4457,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.14580199 = fieldWeight in 4457, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=4457)
      0.5 = coord(1/2)
  0.5 = coord(4/8)
    Abstract
    Google's PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared with simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.
    Source
    Journal of documentation. 59(2003) no.2, S.205-217
  3. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.03 
    0.028500058 = product of:
  0.076000154 = sum of:
    0.04338471 = weight(_text_:retrieval in 2799) [ClassicSimilarity], result of:
      0.04338471 = score(doc=2799,freq=6.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.34732026 = fieldWeight in 2799, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
    0.016396983 = weight(_text_:of in 2799) [ClassicSimilarity], result of:
      0.016396983 = score(doc=2799,freq=12.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.25392252 = fieldWeight in 2799, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
    0.016218461 = product of:
      0.032436922 = sum of:
        0.032436922 = weight(_text_:on in 2799) [ClassicSimilarity], result of:
          0.032436922 = score(doc=2799,freq=12.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.35714048 = fieldWeight in 2799, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=2799)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  4. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.02 
    0.024801197 = product of:
  0.066136524 = sum of:
    0.04338471 = weight(_text_:retrieval in 105) [ClassicSimilarity], result of:
      0.04338471 = score(doc=105,freq=6.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.34732026 = fieldWeight in 105, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.013388081 = weight(_text_:of in 105) [ClassicSimilarity], result of:
      0.013388081 = score(doc=105,freq=8.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.20732689 = fieldWeight in 105, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.009363732 = product of:
      0.018727465 = sum of:
        0.018727465 = weight(_text_:on in 105) [ClassicSimilarity], result of:
          0.018727465 = score(doc=105,freq=4.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.20619515 = fieldWeight in 105, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=105)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    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
    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
  5. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.02 
    0.024764394 = product of:
  0.066038385 = sum of:
    0.047231287 = weight(_text_:retrieval in 7) [ClassicSimilarity], result of:
      0.047231287 = score(doc=7,freq=16.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.37811437 = fieldWeight in 7, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=7)
    0.009978888 = weight(_text_:of in 7) [ClassicSimilarity], result of:
      0.009978888 = score(doc=7,freq=10.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.15453234 = fieldWeight in 7, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=7)
    0.008828212 = product of:
      0.017656423 = sum of:
        0.017656423 = weight(_text_:on in 7) [ClassicSimilarity], result of:
          0.017656423 = score(doc=7,freq=8.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.19440265 = fieldWeight in 7, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.03125 = fieldNorm(doc=7)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.
    Content
    Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading
    RSWK
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
  6. White, R.W.; Jose, J.M.; Ruthven, I.: Using top-ranking sentences to facilitate effective information access (2005) 0.02 
    0.02212434 = product of:
  0.058998242 = sum of:
    0.020873476 = weight(_text_:retrieval in 3881) [ClassicSimilarity], result of:
      0.020873476 = score(doc=3881,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 3881, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3881)
    0.021389665 = weight(_text_:use in 3881) [ClassicSimilarity], result of:
      0.021389665 = score(doc=3881,freq=2.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.1691581 = fieldWeight in 3881, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3881)
    0.0167351 = weight(_text_:of in 3881) [ClassicSimilarity], result of:
      0.0167351 = score(doc=3881,freq=18.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.25915858 = fieldWeight in 3881, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3881)
  0.375 = coord(3/8)
    Abstract
    Web searchers typically fall to view search results beyond the first page nor fully examine those results presented to them. In this article we describe an approach that encourages a deeper examination of the contents of the document set retrieved in response to a searcher's query. The approach shifts the focus of perusal and interaction away from potentially uninformative document surrogates (such as titles, sentence fragments, and URLs) to actual document content, and uses this content to drive the information seeking process. Current search interfaces assume searchers examine results document-by-document. In contrast our approach extracts, ranks, and presents the contents of the top-ranked document set. We use query-relevant topranking sentences extracted from the top documents at retrieval time as fine-grained representations of topranked document content and, when combined in a ranked list, an overview of these documents. The interaction of the searcher provides implicit evidence that is used to reorder the sentences where appropriate. We evaluate our approach in three separate user studies, each applying these sentences in a different way. The findings of these studies show that top-ranking sentences can facilitate effective information access.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.10, S.1113-1125
  7. Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004) 0.02 
    0.021459036 = product of:
  0.057224095 = sum of:
    0.025048172 = weight(_text_:retrieval in 674) [ClassicSimilarity], result of:
      0.025048172 = score(doc=674,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 674, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=674)
    0.018933605 = weight(_text_:of in 674) [ClassicSimilarity], result of:
      0.018933605 = score(doc=674,freq=16.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.2932045 = fieldWeight in 674, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=674)
    0.013242318 = product of:
      0.026484637 = sum of:
        0.026484637 = weight(_text_:on in 674) [ClassicSimilarity], result of:
          0.026484637 = score(doc=674,freq=8.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.29160398 = fieldWeight in 674, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=674)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.
  8. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.02 
    0.019466411 = product of:
  0.077865645 = sum of:
    0.06627123 = weight(_text_:retrieval in 5777) [ClassicSimilarity], result of:
      0.06627123 = score(doc=5777,freq=14.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.5305404 = fieldWeight in 5777, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
    0.011594418 = weight(_text_:of in 5777) [ClassicSimilarity], result of:
      0.011594418 = score(doc=5777,freq=6.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.17955035 = fieldWeight in 5777, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
  0.25 = coord(2/8)
    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
    RSWK
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
  9. Agosti, M.; Pretto, L.: ¬A theoretical study of a generalized version of kleinberg's HITS algorithm (2005) 0.02 
    0.019251872 = product of:
  0.051338322 = sum of:
    0.020873476 = weight(_text_:retrieval in 4) [ClassicSimilarity], result of:
      0.020873476 = score(doc=4,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 4, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4)
    0.024947217 = weight(_text_:of in 4) [ClassicSimilarity], result of:
      0.024947217 = score(doc=4,freq=40.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.38633084 = fieldWeight in 4, product of:
          6.3245554 = tf(freq=40.0), with freq of:
            40.0 = termFreq=40.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4)
    0.0055176322 = product of:
      0.0110352645 = sum of:
        0.0110352645 = weight(_text_:on in 4) [ClassicSimilarity], result of:
          0.0110352645 = score(doc=4,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.121501654 = fieldWeight in 4, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    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.
    Source
    Advances in mathematical/formal methods in information retrieval. 8(2005) no.2 , S.219-243
  10. Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.02 
    0.018241439 = product of:
  0.04864384 = sum of:
    0.020873476 = weight(_text_:retrieval in 2696) [ClassicSimilarity], result of:
      0.020873476 = score(doc=2696,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 2696, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
    0.0167351 = weight(_text_:of in 2696) [ClassicSimilarity], result of:
      0.0167351 = score(doc=2696,freq=18.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.25915858 = fieldWeight in 2696, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
    0.0110352645 = product of:
      0.022070529 = sum of:
        0.022070529 = weight(_text_:on in 2696) [ClassicSimilarity], result of:
          0.022070529 = score(doc=2696,freq=8.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.24300331 = fieldWeight in 2696, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2696)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    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
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  11. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.02 
    0.018009206 = product of:
  0.04802455 = sum of:
    0.02800471 = weight(_text_:retrieval in 6) [ClassicSimilarity], result of:
      0.02800471 = score(doc=6,freq=10.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.22419426 = fieldWeight in 6, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0234375 = fieldNorm(doc=6)
    0.015337974 = weight(_text_:of in 6) [ClassicSimilarity], result of:
      0.015337974 = score(doc=6,freq=42.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.23752278 = fieldWeight in 6, product of:
          6.4807405 = tf(freq=42.0), with freq of:
            42.0 = termFreq=42.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0234375 = fieldNorm(doc=6)
    0.004681866 = product of:
      0.009363732 = sum of:
        0.009363732 = weight(_text_:on in 6) [ClassicSimilarity], result of:
          0.009363732 = score(doc=6,freq=4.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.10309757 = fieldWeight in 6, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0234375 = fieldNorm(doc=6)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.
    Content
    Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
    Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation
  12. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.02 
    0.017924994 = product of:
  0.047799986 = sum of:
    0.025048172 = weight(_text_:retrieval in 3161) [ClassicSimilarity], result of:
      0.025048172 = score(doc=3161,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 3161, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3161)
    0.013388081 = weight(_text_:of in 3161) [ClassicSimilarity], result of:
      0.013388081 = score(doc=3161,freq=8.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.20732689 = fieldWeight in 3161, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3161)
    0.009363732 = product of:
      0.018727465 = sum of:
        0.018727465 = weight(_text_:on in 3161) [ClassicSimilarity], result of:
          0.018727465 = score(doc=3161,freq=4.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.20619515 = fieldWeight in 3161, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3161)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243
  13. Chen, Z.; Meng, X.; Fowler, R.H.; Zhu, B.: Real-time adaptive feature and document learning for Web search (2001) 0.02 
    0.017578287 = product of:
  0.046875432 = sum of:
    0.020873476 = weight(_text_:retrieval in 5209) [ClassicSimilarity], result of:
      0.020873476 = score(doc=5209,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 5209, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5209)
    0.013664153 = weight(_text_:of in 5209) [ClassicSimilarity], result of:
      0.013664153 = score(doc=5209,freq=12.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.21160212 = fieldWeight in 5209, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5209)
    0.012337802 = product of:
      0.024675604 = sum of:
        0.024675604 = weight(_text_:on in 5209) [ClassicSimilarity], result of:
          0.024675604 = score(doc=5209,freq=10.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.271686 = fieldWeight in 5209, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5209)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    Chen et alia report on the design of FEATURES, a web search engine with adaptive features based on minimal relevance feedback. Rather than developing user profiles from previous searcher activity either at the server or client location, or updating indexes after search completion, FEATURES allows for index and user characterization files to be updated during query modification on retrieval from a general purpose search engine. Indexing terms relevant to a query are defined as the union of all terms assigned to documents retrieved by the initial search run and are used to build a vector space model on this retrieved set. The top ten weighted terms are presented to the user for a relevant non-relevant choice which is used to modify the term weights. Documents are chosen if their summed term weights are greater than some threshold. A user evaluation of the top ten ranked documents as non-relevant will decrease these term weights and a positive judgement will increase them. A new ordering of the retrieved set will generate new display lists of terms and documents. Precision is improved in a test on Alta Vista searches.
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.8, S.655-665
  14. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.02 
    0.017257487 = product of:
  0.06902995 = sum of:
    0.050096344 = weight(_text_:retrieval in 3268) [ClassicSimilarity], result of:
      0.050096344 = score(doc=3268,freq=8.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.40105087 = fieldWeight in 3268, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3268)
    0.018933605 = weight(_text_:of in 3268) [ClassicSimilarity], result of:
      0.018933605 = score(doc=3268,freq=16.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.2932045 = fieldWeight in 3268, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3268)
  0.25 = coord(2/8)
    Abstract
    The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.63-69
  15. Watters, C.; Amoudi, A.: Geosearcher : location-based ranking of search engine results (2003) 0.02 
    0.017028242 = product of:
  0.045408648 = sum of:
    0.021389665 = weight(_text_:use in 5152) [ClassicSimilarity], result of:
      0.021389665 = score(doc=5152,freq=2.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.1691581 = fieldWeight in 5152, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5152)
    0.01850135 = weight(_text_:of in 5152) [ClassicSimilarity], result of:
      0.01850135 = score(doc=5152,freq=22.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.28651062 = fieldWeight in 5152, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5152)
    0.0055176322 = product of:
      0.0110352645 = sum of:
        0.0110352645 = weight(_text_:on in 5152) [ClassicSimilarity], result of:
          0.0110352645 = score(doc=5152,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.121501654 = fieldWeight in 5152, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5152)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    Waters and Amoudi describe GeoSearcher, a prototype ranking program that arranges search engine results along a geo-spatial dimension without the provision of geo-spatial meta-tags or the use of geo-spatial feature extraction. GeoSearcher uses URL analysis, IptoLL, Whois, and the Getty Thesaurus of Geographic Names to determine site location. It accepts the first 200 sites returned by a search engine, identifies the coordinates, calculates their distance from a reference point and ranks in ascending order by this value. For any retrieved site the system checks if it has already been located in the current session, then sends the domain name to Whois to generate a return of a two letter country code and an area code. With no success the name is stripped one level and resent. If this fails the top level domain is tested for being a country code. Any remaining unmatched names go to IptoLL. Distance is calculated using the center point of the geographic area and a provided reference location. A test run on a set of 100 URLs from a search was successful in locating 90 sites. Eighty three pages could be manually found and 68 had sufficient information to verify location determination. Of these 65 ( 95%) had been assigned reasonably correct geographic locations. A random set of URLs used instead of a search result, yielded 80% success.
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.2, S.140-151
  16. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.02 
    0.01689653 = product of:
  0.045057412 = sum of:
    0.025048172 = weight(_text_:retrieval in 3455) [ClassicSimilarity], result of:
      0.025048172 = score(doc=3455,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 3455, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3455)
    0.013388081 = weight(_text_:of in 3455) [ClassicSimilarity], result of:
      0.013388081 = score(doc=3455,freq=8.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.20732689 = fieldWeight in 3455, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3455)
    0.006621159 = product of:
      0.013242318 = sum of:
        0.013242318 = weight(_text_:on in 3455) [ClassicSimilarity], result of:
          0.013242318 = score(doc=3455,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.14580199 = fieldWeight in 3455, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3455)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 an the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.571-583
  17. Meghabghab, G.: Google's Web page ranking applied to different topological Web graph structures (2001) 0.02 
    0.016288335 = product of:
  0.04343556 = sum of:
    0.020873476 = weight(_text_:retrieval in 6028) [ClassicSimilarity], result of:
      0.020873476 = score(doc=6028,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 6028, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6028)
    0.014758972 = weight(_text_:of in 6028) [ClassicSimilarity], result of:
      0.014758972 = score(doc=6028,freq=14.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.22855641 = fieldWeight in 6028, 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=6028)
    0.007803111 = product of:
      0.015606222 = sum of:
        0.015606222 = weight(_text_:on in 6028) [ClassicSimilarity], result of:
          0.015606222 = score(doc=6028,freq=4.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.1718293 = fieldWeight in 6028, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6028)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    This research is part of the ongoing study to better understand web page ranking on the web. It looks at a web page as a graph structure or a web graph, and tries to classify different web graphs in the new coordinate space: (out-degree, in-degree). The out-degree coordinate od is defined as the number of outgoing web pages from a given web page. The in-degree id coordinate is the number of web pages that point to a given web page. In this new coordinate space a metric is built to classify how close or far different web graphs are. Google's web ranking algorithm (Brin & Page, 1998) on ranking web pages is applied in this new coordinate space. The results of the algorithm has been modified to fit different topological web graph structures. Also the algorithm was not successful in the case of general web graphs and new ranking web algorithms have to be considered. This study does not look at enhancing web ranking by adding any contextual information. It only considers web links as a source to web page ranking. The author believes that understanding the underlying web page as a graph will help design better ranking web algorithms, enhance retrieval and web performance, and recommends using graphs as a part of visual aid for browsing engine designers
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.9, S.736-747
  18. Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012) 0.02 
    0.01596444 = product of:
  0.04257184 = sum of:
    0.016698781 = weight(_text_:retrieval in 393) [ClassicSimilarity], result of:
      0.016698781 = score(doc=393,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.13368362 = fieldWeight in 393, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=393)
    0.0133880805 = weight(_text_:of in 393) [ClassicSimilarity], result of:
      0.0133880805 = score(doc=393,freq=18.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.20732687 = fieldWeight in 393, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=393)
    0.012484977 = product of:
      0.024969954 = sum of:
        0.024969954 = weight(_text_:on in 393) [ClassicSimilarity], result of:
          0.024969954 = score(doc=393,freq=16.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.27492687 = fieldWeight in 393, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.03125 = fieldNorm(doc=393)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    Abstract
    This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.9, S.1879-1896
  19. 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) 0.01 
    0.014529769 = product of:
  0.03874605 = sum of:
    0.014972764 = weight(_text_:use in 93) [ClassicSimilarity], result of:
      0.014972764 = score(doc=93,freq=2.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.11841066 = fieldWeight in 93, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.02734375 = fieldNorm(doc=93)
    0.019910943 = weight(_text_:of in 93) [ClassicSimilarity], result of:
      0.019910943 = score(doc=93,freq=52.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.30833945 = fieldWeight in 93, product of:
          7.2111025 = tf(freq=52.0), with freq of:
            52.0 = termFreq=52.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=93)
    0.0038623426 = product of:
      0.007724685 = sum of:
        0.007724685 = weight(_text_:on in 93) [ClassicSimilarity], result of:
          0.007724685 = score(doc=93,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.08505116 = fieldWeight in 93, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.02734375 = fieldNorm(doc=93)
      0.5 = coord(1/2)
  0.375 = coord(3/8)
    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.
  20. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
    0.0136958435 = product of:
  0.054783374 = sum of:
    0.015619429 = weight(_text_:of in 3445) [ClassicSimilarity], result of:
      0.015619429 = score(doc=3445,freq=2.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.24188137 = fieldWeight in 3445, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.109375 = fieldNorm(doc=3445)
    0.039163947 = product of:
      0.078327894 = sum of:
        0.078327894 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
          0.078327894 = score(doc=3445,freq=2.0), product of:
            0.1446067 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041294612 = queryNorm
            0.5416616 = fieldWeight in 3445, 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=3445)
      0.5 = coord(1/2)
  0.25 = coord(2/8)
    Date
    25. 8.2005 17:42:22

Authors

Years

Languages

  • e 23
  • d 4

Types

  • a 22
  • m 3
  • el 2
  • r 1
  • More… Less…

Themes

Subjects

Classifications