Search (51 results, page 1 of 3)

0.08332696 = product of:
  0.16665392 = sum of:
    0.055176124 = weight(_text_:wide in 1610) [ClassicSimilarity], result of:
      0.055176124 = score(doc=1610,freq=2.0), product of:
        0.18785246 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.042397358 = queryNorm
        0.29372054 = fieldWeight in 1610, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
    0.06693452 = weight(_text_:web in 1610) [ClassicSimilarity], result of:
      0.06693452 = score(doc=1610,freq=10.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.48375595 = fieldWeight in 1610, 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=1610)
    0.04454327 = weight(_text_:retrieval in 1610) [ClassicSimilarity], result of:
      0.04454327 = score(doc=1610,freq=6.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.34732026 = fieldWeight in 1610, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1610)
  0.5 = coord(3/6)

Abstract

Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information an the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

0.055877417 = product of:
  0.111754835 = sum of:
    0.074835055 = weight(_text_:web in 3471) [ClassicSimilarity], result of:
      0.074835055 = score(doc=3471,freq=18.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.5408555 = fieldWeight in 3471, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3471)
    0.02143089 = weight(_text_:retrieval in 3471) [ClassicSimilarity], result of:
      0.02143089 = score(doc=3471,freq=2.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.16710453 = fieldWeight in 3471, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3471)
    0.015488892 = product of:
      0.046466675 = sum of:
        0.046466675 = weight(_text_:system in 3471) [ClassicSimilarity], result of:
          0.046466675 = score(doc=3471,freq=8.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.3479797 = fieldWeight in 3471, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3471)
      0.33333334 = coord(1/3)
  0.5 = coord(3/6)

Abstract

The unprecedented growth of the Internet has given rise to the Dark Web, the problematic facet of the Web associated with cybercrime, hate, and extremism. Despite the need for tools to collect and analyze Dark Web forums, the covert nature of this part of the Internet makes traditional Web crawling techniques insufficient for capturing such content. In this study, we propose a novel crawling system designed to collect Dark Web forum content. The system uses a human-assisted accessibility approach to gain access to Dark Web forums. Several URL ordering features and techniques enable efficient extraction of forum postings. The system also includes an incremental crawler coupled with a recall-improvement mechanism intended to facilitate enhanced retrieval and updating of collected content. Experiments conducted to evaluate the effectiveness of the human-assisted accessibility approach and the recall-improvement-based, incremental-update procedure yielded favorable results. The human-assisted approach significantly improved access to Dark Web forums while the incremental crawler with recall improvement also outperformed standard periodic- and incremental-update approaches. Using the system, we were able to collect over 100 Dark Web forums from three regions. A case study encompassing link and content analysis of collected forums was used to illustrate the value and importance of gathering and analyzing content from such online communities.

0.049945276 = product of:
  0.14983582 = sum of:
    0.055176124 = weight(_text_:wide in 4242) [ClassicSimilarity], result of:
      0.055176124 = score(doc=4242,freq=2.0), product of:
        0.18785246 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.042397358 = queryNorm
        0.29372054 = fieldWeight in 4242, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
    0.09465969 = weight(_text_:web in 4242) [ClassicSimilarity], result of:
      0.09465969 = score(doc=4242,freq=20.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.6841342 = fieldWeight in 4242, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4242)
  0.33333334 = coord(2/6)

Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

0.044202298 = product of:
  0.1326069 = sum of:
    0.059868045 = weight(_text_:web in 492) [ClassicSimilarity], result of:
      0.059868045 = score(doc=492,freq=2.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.43268442 = fieldWeight in 492, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.07273885 = weight(_text_:retrieval in 492) [ClassicSimilarity], result of:
      0.07273885 = score(doc=492,freq=4.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.5671716 = fieldWeight in 492, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
  0.33333334 = coord(2/6)

Source

Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong

Theme

Klassifikationssysteme im Online-Retrieval

0.039805725 = product of:
  0.119417176 = sum of:
    0.10792866 = weight(_text_:web in 2733) [ClassicSimilarity], result of:
      0.10792866 = score(doc=2733,freq=26.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.780033 = fieldWeight in 2733, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.011488512 = product of:
      0.034465536 = sum of:
        0.034465536 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.034465536 = score(doc=2733,freq=2.0), product of:
            0.14846832 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.042397358 = queryNorm
            0.23214069 = fieldWeight in 2733, 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=2733)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

While the Web has grown significantly in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high-quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories limits users' ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called the Arabic Medical Web Directory (AMedDir) that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab participants and found that the AMedDir significantly outperformed two benchmark Arabic Web directories in terms of browsing effectiveness, efficiency, information quality, and user satisfaction. Participants expressed strong preference for the AMedDir and provided many positive comments. This research thus contributes to developing a useful Web directory for organizing the information in the Arabic medical domain and to a better understanding of how to support browsing on the underdeveloped Web.

Date

22. 3.2009 17:57:50

0.035694227 = product of:
  0.10708268 = sum of:
    0.045980107 = weight(_text_:wide in 142) [ClassicSimilarity], result of:
      0.045980107 = score(doc=142,freq=2.0), product of:
        0.18785246 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.042397358 = queryNorm
        0.24476713 = fieldWeight in 142, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
    0.06110257 = weight(_text_:web in 142) [ClassicSimilarity], result of:
      0.06110257 = score(doc=142,freq=12.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.4416067 = fieldWeight in 142, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
  0.33333334 = coord(2/6)

Abstract

The World Wide Web presents significant opportunities for business intelligence analysis as it can provide information about a company's external environment and its stakeholders. Traditional business intelligence analysis on the Web has focused on simple keyword searching. Recently, it has been suggested that the incoming links, or backlinks, of a company's Web site (i.e., other Web pages that have a hyperlink pointing to the company of Interest) can provide important insights about the company's "online communities." Although analysis of these communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. In this article, we present a tool called Redips that automatically integrates backlink meta-searching and text-mining techniques to facilitate users in performing such business intelligence analysis on the Web. The architectural design and implementation of the tool are presented in the article. To evaluate the effectiveness, efficiency, and user satisfaction of Redips, an experiment was conducted to compare the tool with two popular business Intelligence analysis methods-using backlink search engines and manual browsing. The experiment results showed that Redips was statistically more effective than both benchmark methods (in terms of Recall and F-measure) but required more time in search tasks. In terms of user satisfaction, Redips scored statistically higher than backlink search engines in all five measures used, and also statistically higher than manual browsing in three measures.

0.03465478 = product of:
  0.10396434 = sum of:
    0.06110257 = weight(_text_:web in 5054) [ClassicSimilarity], result of:
      0.06110257 = score(doc=5054,freq=12.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.4416067 = fieldWeight in 5054, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
    0.04286178 = weight(_text_:retrieval in 5054) [ClassicSimilarity], result of:
      0.04286178 = score(doc=5054,freq=8.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.33420905 = fieldWeight in 5054, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5054)
  0.33333334 = coord(2/6)

Abstract

As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.

0.03271184 = product of:
  0.09813552 = sum of:
    0.076673485 = weight(_text_:retrieval in 3845) [ClassicSimilarity], result of:
      0.076673485 = score(doc=3845,freq=10.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.59785134 = fieldWeight in 3845, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=3845)
    0.021462038 = product of:
      0.064386114 = sum of:
        0.064386114 = weight(_text_:system in 3845) [ClassicSimilarity], result of:
          0.064386114 = score(doc=3845,freq=6.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.48217484 = fieldWeight in 3845, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=3845)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems

0.029003143 = product of:
  0.08700943 = sum of:
    0.04989004 = weight(_text_:web in 1615) [ClassicSimilarity], result of:
      0.04989004 = score(doc=1615,freq=8.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.36057037 = fieldWeight in 1615, 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=1615)
    0.037119392 = weight(_text_:retrieval in 1615) [ClassicSimilarity], result of:
      0.037119392 = score(doc=1615,freq=6.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.28943354 = fieldWeight in 1615, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1615)
  0.33333334 = coord(2/6)

Abstract

The Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be "medically-related." This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or "concept space," and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders an a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLS-systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.

Footnote

Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.027924743 = product of:
  0.083774224 = sum of:
    0.06299369 = weight(_text_:retrieval in 4769) [ClassicSimilarity], result of:
      0.06299369 = score(doc=4769,freq=12.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.49118498 = fieldWeight in 4769, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4769)
    0.02078053 = product of:
      0.06234159 = sum of:
        0.06234159 = weight(_text_:system in 4769) [ClassicSimilarity], result of:
          0.06234159 = score(doc=4769,freq=10.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.46686378 = fieldWeight in 4769, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=4769)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent an geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms

0.027122721 = product of:
  0.08136816 = sum of:
    0.029934023 = weight(_text_:web in 501) [ClassicSimilarity], result of:
      0.029934023 = score(doc=501,freq=2.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.21634221 = fieldWeight in 501, 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=501)
    0.051434137 = weight(_text_:retrieval in 501) [ClassicSimilarity], result of:
      0.051434137 = score(doc=501,freq=8.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.40105087 = fieldWeight in 501, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=501)
  0.33333334 = coord(2/6)

Abstract

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

0.024838781 = product of:
  0.07451634 = sum of:
    0.06110257 = weight(_text_:web in 2699) [ClassicSimilarity], result of:
      0.06110257 = score(doc=2699,freq=12.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.4416067 = fieldWeight in 2699, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2699)
    0.013413775 = product of:
      0.040241323 = sum of:
        0.040241323 = weight(_text_:system in 2699) [ClassicSimilarity], result of:
          0.040241323 = score(doc=2699,freq=6.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.30135927 = fieldWeight in 2699, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2699)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships. Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

0.024580924 = product of:
  0.07374277 = sum of:
    0.06599832 = weight(_text_:web in 871) [ClassicSimilarity], result of:
      0.06599832 = score(doc=871,freq=14.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.47698978 = fieldWeight in 871, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=871)
    0.007744446 = product of:
      0.023233337 = sum of:
        0.023233337 = weight(_text_:system in 871) [ClassicSimilarity], result of:
          0.023233337 = score(doc=871,freq=2.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.17398985 = fieldWeight in 871, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=871)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed 2 Web personal spiders based on best first search and genetic algorithm techniques, respectively. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the Web, based on the links and keyword indexing. A graphical, dynamic, Jav-based interface was developed and is available for Web access. A system architecture for implementing such an agent-spider is presented, followed by deteiled discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, we found both results to be comparable and complementary. In user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first search spider. However, their precision values were not statistically different. The mutation process introduced in genetic algorithms allows users to find other potential relevant homepages that cannot be explored via a conventional local search process. In addition, we found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent

Theme

Web-Agenten

0.020380197 = product of:
  0.06114059 = sum of:
    0.051847253 = weight(_text_:web in 1872) [ClassicSimilarity], result of:
      0.051847253 = score(doc=1872,freq=6.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.37471575 = fieldWeight in 1872, 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=1872)
    0.0092933355 = product of:
      0.027880006 = sum of:
        0.027880006 = weight(_text_:system in 1872) [ClassicSimilarity], result of:
          0.027880006 = score(doc=1872,freq=2.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.20878783 = fieldWeight in 1872, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=1872)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

Despite the rapid growth of text-based computer-mediated communication (CMC), its limitations have rendered the media highly incoherent. This poses problems for content analysis of online discourse archives. Interactional coherence analysis (ICA) attempts to accurately identify and construct CMC interaction networks. In this study, we propose the Hybrid Interactional Coherence (HIC) algorithm for identification of web forum interaction. HIC utilizes a bevy of system and linguistic features, including message header information, quotations, direct address, and lexical relations. Furthermore, several similarity-based methods including a Lexical Match Algorithm (LMA) and a sliding window method are utilized to account for interactional idiosyncrasies. Experiments results on two web forums revealed that the proposed HIC algorithm significantly outperformed comparison techniques in terms of precision, recall, and F-measure at both the forum and thread levels. Additionally, an example was used to illustrate how the improved ICA results can facilitate enhanced social network and role analysis capabilities.

0.016230455 = product of:
  0.048691362 = sum of:
    0.035277586 = weight(_text_:web in 4980) [ClassicSimilarity], result of:
      0.035277586 = score(doc=4980,freq=4.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.25496176 = fieldWeight in 4980, 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=4980)
    0.013413775 = product of:
      0.040241323 = sum of:
        0.040241323 = weight(_text_:system in 4980) [ClassicSimilarity], result of:
          0.040241323 = score(doc=4980,freq=6.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.30135927 = fieldWeight in 4980, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4980)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune system-based technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.

0.015409963 = product of:
  0.046229888 = sum of:
    0.035277586 = weight(_text_:web in 2505) [ClassicSimilarity], result of:
      0.035277586 = score(doc=2505,freq=4.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.25496176 = fieldWeight in 2505, 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=2505)
    0.010952301 = product of:
      0.032856904 = sum of:
        0.032856904 = weight(_text_:system in 2505) [ClassicSimilarity], result of:
          0.032856904 = score(doc=2505,freq=4.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.24605882 = fieldWeight in 2505, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2505)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

To make good decisions, businesses try to gather good intelligence information. Yet managing and processing a large amount of unstructured information and data stand in the way of greater business knowledge. An effective business intelligence tool must be able to access quality information from a variety of sources in a variety of forms, and it must support people as they search for and analyze that information. The EBizPort system was designed to address information needs for the business/IT community. EBizPort's collection-building process is designed to acquire credible, timely, and relevant information. The user interface provides access to collected and metasearched resources using innovative tools for summarization, categorization, and visualization. The effectiveness, efficiency, usability, and information quality of the EBizPort system were measured. EBizPort significantly outperformed Brint, a business search portal, in search effectiveness, information quality, user satisfaction, and usability. Users particularly liked EBizPort's clean and user-friendly interface. Results from our evaluation study suggest that the visualization function added value to the search and analysis process, that the generalizable collection-building technique can be useful for domain-specific information searching an the Web, and that the search interface was important for Web search and browse support.

0.014967011 = product of:
  0.089802064 = sum of:
    0.089802064 = weight(_text_:web in 1880) [ClassicSimilarity], result of:
      0.089802064 = score(doc=1880,freq=18.0), product of:
        0.13836423 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.042397358 = queryNorm
        0.64902663 = fieldWeight in 1880, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=1880)
  0.16666667 = coord(1/6)

Abstract

While the Web has become a worldwide platform for communication, terrorists share their ideology and communicate with members on the Dark Web - the reverse side of the Web used by terrorists. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of terrorist activities hinder effective and efficient analysis of terrorist information on the Web. To improve understanding of terrorist activities, we have developed a novel methodology for collecting and analyzing Dark Web information. The methodology incorporates information collection, analysis, and visualization techniques, and exploits various Web information sources. We applied it to collecting and analyzing information of 39 Jihad Web sites and developed visualization of their site contents, relationships, and activity levels. An expert evaluation showed that the methodology is very useful and promising, having a high potential to assist in investigation and understanding of terrorist activities by producing results that could potentially help guide both policymaking and intelligence research.

0.0149546135 = product of:
  0.04486384 = sum of:
    0.037119392 = weight(_text_:retrieval in 6492) [ClassicSimilarity], result of:
      0.037119392 = score(doc=6492,freq=6.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.28943354 = fieldWeight in 6492, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6492)
    0.007744446 = product of:
      0.023233337 = sum of:
        0.023233337 = weight(_text_:system in 6492) [ClassicSimilarity], result of:
          0.023233337 = score(doc=6492,freq=2.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.17398985 = fieldWeight in 6492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6492)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabulary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7.657 worm-specific terms and a Drosophila fly thesaurus of 15.626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitve study of term association involving 4 biologists, we found that a large percentage (59,6-85,6%) of the terms suggested by the subjects were identified in the cojoined fly-worm thesaurus. However, we found only a small percentage (8,4-18,1%) of the associations suggested by the subjects in the thesaurus

0.014509393 = product of:
  0.043528177 = sum of:
    0.030003246 = weight(_text_:retrieval in 3922) [ClassicSimilarity], result of:
      0.030003246 = score(doc=3922,freq=2.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.23394634 = fieldWeight in 3922, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3922)
    0.01352493 = product of:
      0.04057479 = sum of:
        0.04057479 = weight(_text_:29 in 3922) [ClassicSimilarity], result of:
          0.04057479 = score(doc=3922,freq=2.0), product of:
            0.14914064 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.042397358 = queryNorm
            0.27205724 = fieldWeight in 3922, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3922)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

Digital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely labor-intensive processes for text documents and especially for images. Recently, 800.000 declassified staellite photographs were made available by the US Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques

Date

21. 7.1999 13:48:29

0.012953277 = product of:
  0.03885983 = sum of:
    0.025717068 = weight(_text_:retrieval in 5202) [ClassicSimilarity], result of:
      0.025717068 = score(doc=5202,freq=2.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.20052543 = fieldWeight in 5202, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
    0.01314276 = product of:
      0.03942828 = sum of:
        0.03942828 = weight(_text_:system in 5202) [ClassicSimilarity], result of:
          0.03942828 = score(doc=5202,freq=4.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.29527056 = fieldWeight in 5202, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=5202)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty

Theme

Semantisches Umfeld in Indexierung u. Retrieval