Search (91 results, page 1 of 5)

0.20744473 = product of:
  0.27659297 = sum of:
    0.07805218 = weight(_text_:web in 104) [ClassicSimilarity], result of:
      0.07805218 = score(doc=104,freq=10.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.48375595 = fieldWeight in 104, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
    0.104750924 = weight(_text_:search in 104) [ClassicSimilarity], result of:
      0.104750924 = score(doc=104,freq=14.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.6095997 = fieldWeight in 104, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
    0.09378988 = product of:
      0.18757977 = sum of:
        0.18757977 = weight(_text_:engine in 104) [ClassicSimilarity], result of:
          0.18757977 = score(doc=104,freq=8.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.7092499 = fieldWeight in 104, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=104)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64433.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.20717825 = product of:
  0.27623767 = sum of:
    0.06981198 = weight(_text_:web in 102) [ClassicSimilarity], result of:
      0.06981198 = score(doc=102,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.43268442 = fieldWeight in 102, 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=102)
    0.12520128 = weight(_text_:search in 102) [ClassicSimilarity], result of:
      0.12520128 = score(doc=102,freq=20.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.72861093 = fieldWeight in 102, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=102)
    0.08122443 = product of:
      0.16244885 = sum of:
        0.16244885 = weight(_text_:engine in 102) [ClassicSimilarity], result of:
          0.16244885 = score(doc=102,freq=6.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.6142285 = fieldWeight in 102, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=102)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability to formulate accurate queries, and a low tolerance for considering the relevance of the search results. While simple and easy to use interfaces have made Web search universally accessible, they provide little assistance for people to overcome the difficulties they experience when their information needs are more complex than simple fact-verification. In human-centred Web search, the purpose of the search engine expands from a simple information retrieval engine to a decision support system. People are empowered to take an active role in the search process, with the search engine supporting them in developing a deeper understanding of their information needs, assisting them in crafting and refining their queries, and aiding them in evaluating and exploring the search results. In this chapter, recent research in this domain is outlined and discussed.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64427.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.19575962 = product of:
  0.26101282 = sum of:
    0.050382458 = weight(_text_:web in 5493) [ClassicSimilarity], result of:
      0.050382458 = score(doc=5493,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3122631 = fieldWeight in 5493, 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=5493)
    0.09898031 = weight(_text_:search in 5493) [ClassicSimilarity], result of:
      0.09898031 = score(doc=5493,freq=18.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5760175 = fieldWeight in 5493, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5493)
    0.11165006 = sum of:
      0.07815824 = weight(_text_:engine in 5493) [ClassicSimilarity], result of:
        0.07815824 = score(doc=5493,freq=2.0), product of:
          0.26447627 = queryWeight, product of:
            5.349498 = idf(docFreq=570, maxDocs=44218)
            0.049439456 = queryNorm
          0.29552078 = fieldWeight in 5493, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.349498 = idf(docFreq=570, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5493)
      0.03349182 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
        0.03349182 = score(doc=5493,freq=2.0), product of:
          0.17312855 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.049439456 = queryNorm
          0.19345059 = fieldWeight in 5493, 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=5493)
  0.75 = coord(3/4)

Abstract

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Date

20. 1.2015 18:30:22

0.1756793 = product of:
  0.23423907 = sum of:
    0.029088326 = weight(_text_:web in 3752) [ClassicSimilarity], result of:
      0.029088326 = score(doc=3752,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.18028519 = fieldWeight in 3752, 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=3752)
    0.10942685 = weight(_text_:search in 3752) [ClassicSimilarity], result of:
      0.10942685 = score(doc=3752,freq=22.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.6368113 = fieldWeight in 3752, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3752)
    0.095723905 = product of:
      0.19144781 = sum of:
        0.19144781 = weight(_text_:engine in 3752) [ClassicSimilarity], result of:
          0.19144781 = score(doc=3752,freq=12.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.72387516 = fieldWeight in 3752, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3752)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Web search engines act as gatekeepers when people search for information online. Research has shown that search engine users seem to trust the search engines' ranking uncritically and mostly select top-ranked results. This study further examines search engine users' selection behavior. Drawing from the credibility and information research literature, we test whether the presence or absence of certain credibility cues influences the selection probability of search engine results. In an observational study, participants (N?=?247) completed two information research tasks on preset search engine results pages, on which three credibility cues (source reputation, message neutrality, and social recommendations) as well as the search result ranking were systematically varied. The results of our study confirm the significance of the ranking. Of the three credibility cues, only reputation had an additional effect on selection probabilities. Personal characteristics (prior knowledge about the researched issues, search engine usage patterns, etc.) did not influence the preference for search results linked with certain credibility cues. These findings are discussed in light of situational and contextual characteristics (e.g., involvement, low-cost scenarios).

0.16609338 = product of:
  0.22145784 = sum of:
    0.06981198 = weight(_text_:web in 1233) [ClassicSimilarity], result of:
      0.06981198 = score(doc=1233,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.43268442 = fieldWeight in 1233, 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=1233)
    0.104750924 = weight(_text_:search in 1233) [ClassicSimilarity], result of:
      0.104750924 = score(doc=1233,freq=14.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.6095997 = fieldWeight in 1233, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=1233)
    0.04689494 = product of:
      0.09378988 = sum of:
        0.09378988 = weight(_text_:engine in 1233) [ClassicSimilarity], result of:
          0.09378988 = score(doc=1233,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.35462496 = fieldWeight in 1233, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=1233)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Searches conducted on web search engines reflect the interests of users and society. Google Trends, which provides information about the queries searched by users of the Google web search engine, is a rich data source from which a wealth of information can be mined. We investigated the possibility of using web search volume data from Google Trends to predict academic fame. As queries are language-dependent, we studied universities from two countries with different languages, the United States and Spain. We found a significant correlation between the search volume of a university name and the university's academic reputation or fame. We also examined the effect of some Google Trends features, namely, limiting the search to a specific country or topic category on the search volume data. Finally, we examined the effect of university sizes on the correlations found to gain a deeper understanding of the nature of the relationships.

0.15349582 = product of:
  0.2046611 = sum of:
    0.03490599 = weight(_text_:web in 2157) [ClassicSimilarity], result of:
      0.03490599 = score(doc=2157,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 2157, 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=2157)
    0.08853068 = weight(_text_:search in 2157) [ClassicSimilarity], result of:
      0.08853068 = score(doc=2157,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.51520574 = fieldWeight in 2157, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=2157)
    0.08122443 = product of:
      0.16244885 = sum of:
        0.16244885 = weight(_text_:engine in 2157) [ClassicSimilarity], result of:
          0.16244885 = score(doc=2157,freq=6.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.6142285 = fieldWeight in 2157, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=2157)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Search engine retrieval effectiveness studies are usually small scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, and data were collected using specialized software, the Relevance Assessment Tool (RAT). We found that although Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases, whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, because users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.

0.15316099 = product of:
  0.20421466 = sum of:
    0.049364526 = weight(_text_:web in 10) [ClassicSimilarity], result of:
      0.049364526 = score(doc=10,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3059541 = fieldWeight in 10, 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=10)
    0.08853068 = weight(_text_:search in 10) [ClassicSimilarity], result of:
      0.08853068 = score(doc=10,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.51520574 = fieldWeight in 10, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=10)
    0.06631946 = product of:
      0.13263892 = sum of:
        0.13263892 = weight(_text_:engine in 10) [ClassicSimilarity], result of:
          0.13263892 = score(doc=10,freq=4.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.5015154 = fieldWeight in 10, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=10)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.

0.15254721 = product of:
  0.30509442 = sum of:
    0.07465562 = weight(_text_:search in 1149) [ClassicSimilarity], result of:
      0.07465562 = score(doc=1149,freq=4.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.43445963 = fieldWeight in 1149, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0625 = fieldNorm(doc=1149)
    0.2304388 = sum of:
      0.1768519 = weight(_text_:engine in 1149) [ClassicSimilarity], result of:
        0.1768519 = score(doc=1149,freq=4.0), product of:
          0.26447627 = queryWeight, product of:
            5.349498 = idf(docFreq=570, maxDocs=44218)
            0.049439456 = queryNorm
          0.6686872 = fieldWeight in 1149, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.349498 = idf(docFreq=570, maxDocs=44218)
            0.0625 = fieldNorm(doc=1149)
      0.053586908 = weight(_text_:22 in 1149) [ClassicSimilarity], result of:
        0.053586908 = score(doc=1149,freq=2.0), product of:
          0.17312855 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.049439456 = queryNorm
          0.30952093 = fieldWeight in 1149, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0625 = fieldNorm(doc=1149)
  0.5 = coord(2/4)

Abstract

This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.

Date

17.12.2013 11:02:22

0.15173718 = product of:
  0.20231625 = sum of:
    0.050382458 = weight(_text_:web in 3144) [ClassicSimilarity], result of:
      0.050382458 = score(doc=3144,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3122631 = fieldWeight in 3144, 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=3144)
    0.07377557 = weight(_text_:search in 3144) [ClassicSimilarity], result of:
      0.07377557 = score(doc=3144,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.4293381 = fieldWeight in 3144, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3144)
    0.07815824 = product of:
      0.15631647 = sum of:
        0.15631647 = weight(_text_:engine in 3144) [ClassicSimilarity], result of:
          0.15631647 = score(doc=3144,freq=8.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.59104156 = fieldWeight in 3144, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3144)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

0.14231709 = product of:
  0.18975613 = sum of:
    0.03490599 = weight(_text_:web in 279) [ClassicSimilarity], result of:
      0.03490599 = score(doc=279,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 279, 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=279)
    0.08853068 = weight(_text_:search in 279) [ClassicSimilarity], result of:
      0.08853068 = score(doc=279,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.51520574 = fieldWeight in 279, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=279)
    0.06631946 = product of:
      0.13263892 = sum of:
        0.13263892 = weight(_text_:engine in 279) [ClassicSimilarity], result of:
          0.13263892 = score(doc=279,freq=4.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.5015154 = fieldWeight in 279, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=279)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k-means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates.

0.1399036 = product of:
  0.18653813 = sum of:
    0.060458954 = weight(_text_:web in 95) [ClassicSimilarity], result of:
      0.060458954 = score(doc=95,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.37471575 = fieldWeight in 95, 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=95)
    0.07918424 = weight(_text_:search in 95) [ClassicSimilarity], result of:
      0.07918424 = score(doc=95,freq=8.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.460814 = fieldWeight in 95, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
    0.04689494 = product of:
      0.09378988 = sum of:
        0.09378988 = weight(_text_:engine in 95) [ClassicSimilarity], result of:
          0.09378988 = score(doc=95,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.35462496 = fieldWeight in 95, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=95)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

In this chapter, the authors describe the key indexing components of today's web search engines. As the World Wide Web has grown, the systems and methods for indexing have changed significantly. The authors present the data structures used, the features extracted, the infrastructure needed, and the options available for designing a brand new search engine. Techniques are highlighted that improve relevance of results, discuss trade-offs to best utilize machine resources, and cover distributed processing concepts in this context. In particular, the authors delve into the topics of indexing phrases instead of terms, storage in memory vs. on disk, and data partitioning. Some thoughts on information organization for the newly emerging data-forms conclude the chapter.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64418.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.13870476 = product of:
  0.18493968 = sum of:
    0.06504348 = weight(_text_:web in 3188) [ClassicSimilarity], result of:
      0.06504348 = score(doc=3188,freq=10.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.40312994 = fieldWeight in 3188, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
    0.08081709 = weight(_text_:search in 3188) [ClassicSimilarity], result of:
      0.08081709 = score(doc=3188,freq=12.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.47031635 = fieldWeight in 3188, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
    0.03907912 = product of:
      0.07815824 = sum of:
        0.07815824 = weight(_text_:engine in 3188) [ClassicSimilarity], result of:
          0.07815824 = score(doc=3188,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.29552078 = fieldWeight in 3188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3188)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.

0.13841115 = product of:
  0.1845482 = sum of:
    0.05817665 = weight(_text_:web in 2726) [ClassicSimilarity], result of:
      0.05817665 = score(doc=2726,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.36057037 = fieldWeight in 2726, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2726)
    0.08729243 = weight(_text_:search in 2726) [ClassicSimilarity], result of:
      0.08729243 = score(doc=2726,freq=14.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5079997 = fieldWeight in 2726, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2726)
    0.03907912 = product of:
      0.07815824 = sum of:
        0.07815824 = weight(_text_:engine in 2726) [ClassicSimilarity], result of:
          0.07815824 = score(doc=2726,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.29552078 = fieldWeight in 2726, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2726)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

This research investigates how people's perceptions of information retrieval (IR) systems, their perceptions of search tasks, and their perceptions of self-efficacy influence the amount of invested mental effort (AIME) they put into using two different IR systems: a Web search engine and a library system. It also explores the impact of mental effort on an end user's search experience. To assess AIME in online searching, two experiments were conducted using these methods: Experiment 1 relied on self-reports and Experiment 2 employed the dual-task technique. In both experiments, data were collected through search transaction logs, a pre-search background questionnaire, a post-search questionnaire and an interview. Important findings are these: (1) subjects invested greater mental effort searching a library system than searching the Web; (2) subjects put little effort into Web searching because of their high sense of self-efficacy in their searching ability and their perception of the easiness of the Web; (3) subjects did not recognize that putting mental effort into searching was something needed to improve the search results; and (4) data collected from multiple sources proved to be effective for assessing mental effort in online searching.

0.13777182 = product of:
  0.18369575 = sum of:
    0.041137107 = weight(_text_:web in 100) [ClassicSimilarity], result of:
      0.041137107 = score(doc=100,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 100, 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=100)
    0.08729243 = weight(_text_:search in 100) [ClassicSimilarity], result of:
      0.08729243 = score(doc=100,freq=14.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5079997 = fieldWeight in 100, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=100)
    0.05526622 = product of:
      0.11053244 = sum of:
        0.11053244 = weight(_text_:engine in 100) [ClassicSimilarity], result of:
          0.11053244 = score(doc=100,freq=4.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.41792953 = fieldWeight in 100, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=100)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Over the last years, research and industry players have become increasingly interested in analyzing opinions and sentiments expressed on the social media web for product marketing and business intelligence. In order to adapt to this need search engines not only have to be able to retrieve lists of documents but to directly access, analyze, and interpret topics and opinions. This article covers an intermediate phase of the ongoing industrial research project 'DoXa' aiming at developing a semantic opinion and sentiment mining search engine for the French language. The DoXa search engine enables topic related opinion and sentiment extraction beyond positive and negative polarity using rich linguistic resources. Centering the work on two distinct business use cases, the authors analyze both unstructured Web 2.0 contents (e.g., blogs and forums) and structured questionnaire data sets. The focus is on discovering hidden patterns in the data. To this end, the authors present work in progress on opinion topic relation extraction and visual analytics, linguistic resource construction as well as the combination of OLAP technology with semantic search.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64426.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.13694978 = product of:
  0.1825997 = sum of:
    0.041137107 = weight(_text_:web in 3325) [ClassicSimilarity], result of:
      0.041137107 = score(doc=3325,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 3325, 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=3325)
    0.07377557 = weight(_text_:search in 3325) [ClassicSimilarity], result of:
      0.07377557 = score(doc=3325,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.4293381 = fieldWeight in 3325, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3325)
    0.06768702 = product of:
      0.13537404 = sum of:
        0.13537404 = weight(_text_:engine in 3325) [ClassicSimilarity], result of:
          0.13537404 = score(doc=3325,freq=6.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.51185703 = fieldWeight in 3325, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3325)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.

0.13110825 = product of:
  0.174811 = sum of:
    0.041137107 = weight(_text_:web in 385) [ClassicSimilarity], result of:
      0.041137107 = score(doc=385,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 385, 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=385)
    0.06598687 = weight(_text_:search in 385) [ClassicSimilarity], result of:
      0.06598687 = score(doc=385,freq=8.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.3840117 = fieldWeight in 385, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=385)
    0.06768702 = product of:
      0.13537404 = sum of:
        0.13537404 = weight(_text_:engine in 385) [ClassicSimilarity], result of:
          0.13537404 = score(doc=385,freq=6.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.51185703 = fieldWeight in 385, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=385)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

The purpose of this article is to test the reliability of query intents derived from queries, either by the user who entered the query or by another juror. We report the findings of three studies. First, we conducted a large-scale classification study (~50,000 queries) using a crowdsourcing approach. Next, we used clickthrough data from a search engine log and validated the judgments given by the jurors from the crowdsourcing study. Finally, we conducted an online survey on a commercial search engine's portal. Because we used the same queries for all three studies, we also were able to compare the results and the effectiveness of the different approaches. We found that neither the crowdsourcing approach, using jurors who classified queries originating from other users, nor the questionnaire approach, using searchers who were asked about their own query that they just entered into a Web search engine, led to satisfying results. This leads us to conclude that there was little understanding of the classification tasks, even though both groups of jurors were given detailed instructions. Although we used manual classification, our research also has important implications for automatic classification. We must question the success of approaches using automatic classification and comparing its performance to a baseline from human jurors.

0.12735078 = product of:
  0.16980103 = sum of:
    0.03490599 = weight(_text_:web in 3185) [ClassicSimilarity], result of:
      0.03490599 = score(doc=3185,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 3185, 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=3185)
    0.068575576 = weight(_text_:search in 3185) [ClassicSimilarity], result of:
      0.068575576 = score(doc=3185,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.39907667 = fieldWeight in 3185, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=3185)
    0.06631946 = product of:
      0.13263892 = sum of:
        0.13263892 = weight(_text_:engine in 3185) [ClassicSimilarity], result of:
          0.13263892 = score(doc=3185,freq=4.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.5015154 = fieldWeight in 3185, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=3185)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.

0.12735078 = product of:
  0.16980103 = sum of:
    0.03490599 = weight(_text_:web in 5100) [ClassicSimilarity], result of:
      0.03490599 = score(doc=5100,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.21634221 = fieldWeight in 5100, 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=5100)
    0.068575576 = weight(_text_:search in 5100) [ClassicSimilarity], result of:
      0.068575576 = score(doc=5100,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.39907667 = fieldWeight in 5100, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=5100)
    0.06631946 = product of:
      0.13263892 = sum of:
        0.13263892 = weight(_text_:engine in 5100) [ClassicSimilarity], result of:
          0.13263892 = score(doc=5100,freq=4.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.5015154 = fieldWeight in 5100, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.046875 = fieldNorm(doc=5100)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.

0.12563148 = product of:
  0.16750865 = sum of:
    0.041137107 = weight(_text_:web in 3854) [ClassicSimilarity], result of:
      0.041137107 = score(doc=3854,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 3854, 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=3854)
    0.08729243 = weight(_text_:search in 3854) [ClassicSimilarity], result of:
      0.08729243 = score(doc=3854,freq=14.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5079997 = fieldWeight in 3854, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3854)
    0.03907912 = product of:
      0.07815824 = sum of:
        0.07815824 = weight(_text_:engine in 3854) [ClassicSimilarity], result of:
          0.07815824 = score(doc=3854,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.29552078 = fieldWeight in 3854, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3854)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Web search engines, such as Google and Bing, are constantly employing results from knowledge organization and various visualization features to improve their search services. Knowledge graph, a large repository of structured knowledge represented by formal languages such as RDF (Resource Description Framework), is used to support entity search feature of Google and Bing (Demartini, 2016). When a user searchs for an entity, such as a person, an organization, or a place in Google or Bing, it is likely that a knowledge cardwill be presented on the right side bar of the search engine result pages (SERPs). For example, when a user searches the entity Benedict Cumberbatch on Google, the knowledge card will show the basic structured information about this person, including his date of birth, height, spouse, parents, and his movies, etc. The knowledge card, which is used to present the result of entity search, is generated from knowledge graphs. Therefore, the quality of knowledge graphs is essential to the performance of entity search. However, studies on the quality of knowledge graphs from the angle of entity coverage are scant in the literature. This study aims to investigate the coverage of entities of knowledge graphs behind Google and Bing.

0.1253608 = product of:
  0.16714774 = sum of:
    0.029088326 = weight(_text_:web in 4537) [ClassicSimilarity], result of:
      0.029088326 = score(doc=4537,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.18028519 = fieldWeight in 4537, 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=4537)
    0.09898031 = weight(_text_:search in 4537) [ClassicSimilarity], result of:
      0.09898031 = score(doc=4537,freq=18.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.5760175 = fieldWeight in 4537, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4537)
    0.03907912 = product of:
      0.07815824 = sum of:
        0.07815824 = weight(_text_:engine in 4537) [ClassicSimilarity], result of:
          0.07815824 = score(doc=4537,freq=2.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.29552078 = fieldWeight in 4537, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4537)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

Purpose - The purpose of this paper is to test major web search engines on their performance on navigational queries, i.e. searches for homepages. Design/methodology/approach - In total, 100 user queries are posed to six search engines (Google, Yahoo!, MSN, Ask, Seekport, and Exalead). Users described the desired pages, and the results position of these was recorded. Measured success and mean reciprocal rank are calculated. Findings - The performance of the major search engines Google, Yahoo!, and MSN was found to be the best, with around 90 per cent of queries answered correctly. Ask and Exalead performed worse but received good scores as well. Research limitations/implications - All queries were in German, and the German-language interfaces of the search engines were used. Therefore, the results are only valid for German queries. Practical implications - When designing a search engine to compete with the major search engines, care should be taken on the performance on navigational queries. Users can be influenced easily in their quality ratings of search engines based on this performance. Originality/value - This study systematically compares the major search engines on navigational queries and compares the findings with studies on the retrieval effectiveness of the engines on informational queries.