Search (261 results, page 1 of 14)

0.19495897 = product of:
  0.38991794 = sum of:
    0.07437435 = product of:
      0.22312303 = sum of:
        0.22312303 = weight(_text_:3a in 2514) [ClassicSimilarity], result of:
          0.22312303 = score(doc=2514,freq=2.0), product of:
            0.39700332 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046827413 = queryNorm
            0.56201804 = fieldWeight in 2514, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=2514)
      0.33333334 = coord(1/3)
    0.3155436 = weight(_text_:2f in 2514) [ClassicSimilarity], result of:
      0.3155436 = score(doc=2514,freq=4.0), product of:
        0.39700332 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046827413 = queryNorm
        0.7948135 = fieldWeight in 2514, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=2514)
  0.5 = coord(2/4)

Content

Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.

0.08185689 = product of:
  0.16371378 = sum of:
    0.08959906 = weight(_text_:data in 1605) [ClassicSimilarity], result of:
      0.08959906 = score(doc=1605,freq=24.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.60511017 = fieldWeight in 1605, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.07411472 = sum of:
      0.042392377 = weight(_text_:processing in 1605) [ClassicSimilarity], result of:
        0.042392377 = score(doc=1605,freq=2.0), product of:
          0.18956426 = queryWeight, product of:
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.046827413 = queryNorm
          0.22363065 = fieldWeight in 1605, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1605)
      0.03172234 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
        0.03172234 = score(doc=1605,freq=2.0), product of:
          0.16398162 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046827413 = queryNorm
          0.19345059 = fieldWeight in 1605, 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=1605)
  0.5 = coord(2/4)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Theme

Data Mining

0.03959743 = product of:
  0.07919486 = sum of:
    0.053759433 = weight(_text_:data in 95) [ClassicSimilarity], result of:
      0.053759433 = score(doc=95,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.3630661 = fieldWeight in 95, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 95) [ClassicSimilarity], result of:
          0.05087085 = score(doc=95,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 95, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=95)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

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.

0.038636878 = product of:
  0.077273756 = sum of:
    0.04138403 = weight(_text_:data in 3601) [ClassicSimilarity], result of:
      0.04138403 = score(doc=3601,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2794884 = fieldWeight in 3601, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=3601)
    0.03588973 = product of:
      0.07177946 = sum of:
        0.07177946 = weight(_text_:22 in 3601) [ClassicSimilarity], result of:
          0.07177946 = score(doc=3601,freq=4.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.4377287 = fieldWeight in 3601, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=3601)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Explains how 5 Internet search engines (AltaVista, HotBot, InfoSeek, Lycos, and Yahoo) handle field searching. Includes a chart which identifies where on a search engine's page a particular field is searched and the prefix syntax used, and gives examples. Details the individual fields that can be searched: data, title, URL, images, audiovideo and other page content, links and page depth

Source

Online. 22(1998) no.3, S.18-22

0.033504575 = product of:
  0.06700915 = sum of:
    0.031038022 = weight(_text_:data in 5777) [ClassicSimilarity], result of:
      0.031038022 = score(doc=5777,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2096163 = fieldWeight in 5777, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=5777)
    0.035971127 = product of:
      0.071942255 = sum of:
        0.071942255 = weight(_text_:processing in 5777) [ClassicSimilarity], result of:
          0.071942255 = score(doc=5777,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3795138 = fieldWeight in 5777, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=5777)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This book discusses many of the key design issues for building search engines and emphazises the important role that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms, and software but also user-centered issues such as interfaces, manual indexing, and document preparation. They also present some of the current problems in information retrieval that many not be familiar to applied mathematicians and computer scientists and some of the driving computational methods (SVD, SDD) for automated conceptual indexing

LCSH

Text processing (Computer science)

Subject

Text processing (Computer science)

0.032997858 = product of:
  0.065995716 = sum of:
    0.04479953 = weight(_text_:data in 1210) [ClassicSimilarity], result of:
      0.04479953 = score(doc=1210,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.30255508 = fieldWeight in 1210, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1210)
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 1210) [ClassicSimilarity], result of:
          0.042392377 = score(doc=1210,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 1210, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1210)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

0.032942846 = product of:
  0.06588569 = sum of:
    0.036211025 = weight(_text_:data in 1047) [ClassicSimilarity], result of:
      0.036211025 = score(doc=1047,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24455236 = fieldWeight in 1047, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1047)
    0.029674664 = product of:
      0.05934933 = sum of:
        0.05934933 = weight(_text_:processing in 1047) [ClassicSimilarity], result of:
          0.05934933 = score(doc=1047,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3130829 = fieldWeight in 1047, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1047)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Source

Information processing and management. 41(2005) no.5, S.1243-1262

0.030330349 = product of:
  0.060660698 = sum of:
    0.04479953 = weight(_text_:data in 5288) [ClassicSimilarity], result of:
      0.04479953 = score(doc=5288,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.30255508 = fieldWeight in 5288, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5288)
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
          0.03172234 = score(doc=5288,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 5288, 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=5288)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google's top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.

Date

20. 1.2015 18:30:22

0.029208332 = product of:
  0.058416665 = sum of:
    0.036211025 = weight(_text_:data in 3647) [ClassicSimilarity], result of:
      0.036211025 = score(doc=3647,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24455236 = fieldWeight in 3647, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3647)
    0.022205638 = product of:
      0.044411276 = sum of:
        0.044411276 = weight(_text_:22 in 3647) [ClassicSimilarity], result of:
          0.044411276 = score(doc=3647,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.2708308 = fieldWeight in 3647, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3647)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The days of the traditional abstracting and indexing services are waning, as abstracts and bibliographic data become commodities. However, there are tremedous opportunities for those organizations willing to look beyond the status quo to the new possibilities enabled by the latest wave of advanced technologies. Those who own content need to focus on the delivery mechanisms and new markets that technology can provide. Features like automatic extraction of key concepts or names, collaborative filtering to help with trend analysis, and visualization techniques can take information past the retrieval stage and into the management area

Source

Database. 21(1998) no.4, S.18-22

0.028887425 = product of:
  0.05777485 = sum of:
    0.03657866 = weight(_text_:data in 2726) [ClassicSimilarity], result of:
      0.03657866 = score(doc=2726,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24703519 = fieldWeight in 2726, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2726)
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 2726) [ClassicSimilarity], result of:
          0.042392377 = score(doc=2726,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 2726, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2726)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

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.

Source

Information processing and management. 48(2012) no.6, S.1136-1150

0.026219916 = product of:
  0.05243983 = sum of:
    0.03657866 = weight(_text_:data in 5283) [ClassicSimilarity], result of:
      0.03657866 = score(doc=5283,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24703519 = fieldWeight in 5283, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5283)
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 5283) [ClassicSimilarity], result of:
          0.03172234 = score(doc=5283,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 5283, 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=5283)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

Date

22. 7.2006 16:59:13

0.025035713 = product of:
  0.050071426 = sum of:
    0.031038022 = weight(_text_:data in 4873) [ClassicSimilarity], result of:
      0.031038022 = score(doc=4873,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2096163 = fieldWeight in 4873, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=4873)
    0.019033402 = product of:
      0.038066804 = sum of:
        0.038066804 = weight(_text_:22 in 4873) [ClassicSimilarity], result of:
          0.038066804 = score(doc=4873,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.23214069 = fieldWeight in 4873, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4873)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Project SSG-FI at SUB Göttingen provides special subject gateways to international high quality Internet resources for scientific users. Internet sites are selected by subject specialists and described using an extension of qualified Dublin Core metadata. A basic evaluation is added. These descriptions are freely available and can be searched and browsed. These are now subject gateways for 3 subject ares: earth sciences (GeoGuide); mathematics (MathGuide); and Anglo-American culture (split into HistoryGuide and AnglistikGuide). Together they receive about 3.300 'hard' requests per day, thus reaching over 1 million requests per year. The project SSG-FI behind these guides is open to collaboration. Institutions and private persons wishing to contribute can notify the SSG-FI team or send full data sets. Regular contributors can request registration with the project to access the database via the Internet and create and edit records

Date

22. 6.2002 19:40:42

0.023530604 = product of:
  0.04706121 = sum of:
    0.02586502 = weight(_text_:data in 3910) [ClassicSimilarity], result of:
      0.02586502 = score(doc=3910,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 3910, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3910)
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 3910) [ClassicSimilarity], result of:
          0.042392377 = score(doc=3910,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 3910, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3910)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Web queries in question format are becoming a common element of a user's interaction with Web search engines. Web search services such as Ask Jeeves - a publicly accessible question and answer (Q&A) search engine - request users to enter question format queries. This paper provides results from a study examining queries in question format submitted to two different Web search engines - Ask Jeeves that explicitly encourages queries in question format and the Excite search service that does not explicitly encourage queries in question format. We identify the characteristics of queries in question format in two different data sets: (1) 30,000 Ask Jeeves queries and 15,575 Excite queries, including the nature, length, and structure of queries in question format. Findings include: (1) 50% of Ask Jeeves queries and less than 1% of Excite were in question format, (2) most users entered only one query in question format with little query reformulation, (3) limited range of formats for queries in question format - mainly "where", "what", or "how" questions, (4) most common question query format was "Where can I find ..." for general information on a topic, and (5) non-question queries may be in request format. Overall, four types of user Web queries were identified: keyword, Boolean, question, and request. These findings provide an initial mapping of the structure and content of queries in question and request format. Implications for Web search services are discussed.

Source

Information processing and management. 38(2002) no.4, S.453-471

0.023530604 = product of:
  0.04706121 = sum of:
    0.02586502 = weight(_text_:data in 1068) [ClassicSimilarity], result of:
      0.02586502 = score(doc=1068,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 1068, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1068)
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 1068) [ClassicSimilarity], result of:
          0.042392377 = score(doc=1068,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 1068, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1068)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results. We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall's W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.

Source

Information processing and management. 41(2005) no.6, S.1511-1519

0.022336382 = product of:
  0.044672765 = sum of:
    0.020692015 = weight(_text_:data in 7) [ClassicSimilarity], result of:
      0.020692015 = score(doc=7,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.1397442 = fieldWeight in 7, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03125 = fieldNorm(doc=7)
    0.02398075 = product of:
      0.0479615 = sum of:
        0.0479615 = weight(_text_:processing in 7) [ClassicSimilarity], result of:
          0.0479615 = score(doc=7,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.2530092 = fieldWeight in 7, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.03125 = fieldNorm(doc=7)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

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.

LCSH

Text processing (Computer science)

Subject

Text processing (Computer science)

0.020863095 = product of:
  0.04172619 = sum of:
    0.02586502 = weight(_text_:data in 2117) [ClassicSimilarity], result of:
      0.02586502 = score(doc=2117,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 2117, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2117)
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 2117) [ClassicSimilarity], result of:
          0.03172234 = score(doc=2117,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 2117, 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=2117)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This paper presents an application of the model described in Part I to the evaluation of Web search engines by undergraduates. The study observed how 36 undergraduate used four major search engines to find information for their own individual problems and how they evaluated these engines based an actual interaction with the search engines. User evaluation was based an 16 performance measures representing five evaluation criteria: relevance, efficiency, utility, user satisfaction, and connectivity. Non-performance (user-related) measures were also applied. Each participant searched his/ her own topic an all four engines and provided satisfaction ratings for system features and interaction and reasons for satisfaction. Each also made relevance judgements of retrieved items in relation to his/her own information need and participated in post-search Interviews to provide reactions to the search results and overall performance. The study found significant differences in precision PR1 relative recall, user satisfaction with output display, time saving, value of search results, and overall performance among the four engines and also significant engine by discipline interactions an all these measures. In addition, the study found significant differences in user satisfaction with response time among four engines, and significant engine by discipline interaction in user satisfaction with search interface. None of the four search engines dominated in every aspect of the multidimensional evaluation. Content analysis of verbal data identified a number of user criteria and users evaluative comments based an these criteria. Results from both quantitative analysis and content analysis provide insight for system design and development, and useful feedback an strengths and weaknesses of search engines for system improvement

Date

24. 1.2004 18:27:22

0.020863095 = product of:
  0.04172619 = sum of:
    0.02586502 = weight(_text_:data in 2565) [ClassicSimilarity], result of:
      0.02586502 = score(doc=2565,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 2565, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2565)
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 2565) [ClassicSimilarity], result of:
          0.03172234 = score(doc=2565,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 2565, 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=2565)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family. The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths. We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data. Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's tau on large domain datasets.

Date

16. 1.2016 10:22:28

0.020529725 = product of:
  0.0821189 = sum of:
    0.0821189 = weight(_text_:data in 96) [ClassicSimilarity], result of:
      0.0821189 = score(doc=96,freq=14.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.55459267 = fieldWeight in 96, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=96)
  0.25 = coord(1/4)

Abstract

E-sciences are data-intensive sciences that make a large use of the Web to share, collect, and process data. In this context, primary scientific data is becoming a new challenging issue as data must be extensively described (1) to account for empiric conditions and results that allow interpretation and/or analyses and (2) to be understandable by computers used for data storage and information retrieval. With this respect, metadata is a focal point whatever it is considered from the point of view of the user to visualize and exploit data as well as this of the search tools to find and retrieve information. Numerous disciplines are concerned with the issues of describing complex observations and addressing pertinent knowledge. In this paper, similarities and differences in data description and exploration strategies among disciplines in e-sciences are examined.

0.01996638 = product of:
  0.03993276 = sum of:
    0.021947198 = weight(_text_:data in 6) [ClassicSimilarity], result of:
      0.021947198 = score(doc=6,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.14822112 = fieldWeight in 6, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0234375 = fieldNorm(doc=6)
    0.017985564 = product of:
      0.035971127 = sum of:
        0.035971127 = weight(_text_:processing in 6) [ClassicSimilarity], result of:
          0.035971127 = score(doc=6,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.1897569 = fieldWeight in 6, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0234375 = fieldNorm(doc=6)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

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

0.016690476 = product of:
  0.03338095 = sum of:
    0.020692015 = weight(_text_:data in 1188) [ClassicSimilarity], result of:
      0.020692015 = score(doc=1188,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.1397442 = fieldWeight in 1188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03125 = fieldNorm(doc=1188)
    0.012688936 = product of:
      0.025377871 = sum of:
        0.025377871 = weight(_text_:22 in 1188) [ClassicSimilarity], result of:
          0.025377871 = score(doc=1188,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.15476047 = fieldWeight in 1188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1188)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Surfing the World Wide Web used to be cool, dude, real cool. But things have gotten hot - so hot that finding something useful an the Web is no longer cool. It is suffocating Web searchers in the smoke and debris of mountain-sized lists of hits, decisions about which search engines they should use, whether they will get lost in the dizzying maze of a subject directory, use the right syntax for the search engine at hand, enter keywords that are likely to retrieve hits an the topics they have in mind, or enlist a browser that has sufficient functionality to display the most promising hits. When it comes to Web searching, in a few short years we have gone from the cool image of surfing the Web into the frying pan of searching the Web. We can turn down the heat by rethinking what Web searchers are doing and introduce some order into the chaos. Web search strategies that are tool-based-oriented to specific Web searching tools such as search en gines, subject directories, and meta search engines-have been widely promoted, and these strategies are just not working. It is time to dissect what Web searching tools expect from searchers and adjust our search strategies to these new tools. This discussion offers Web searchers help in the form of search strategies that are based an strategies that librarians have been using for a long time to search commercial information retrieval systems like Dialog, NEXIS, Wilsonline, FirstSearch, and Data-Star.

Date

22. 9.1997 19:16:05