Search (59 results, page 1 of 3)

0.041115336 = product of:
  0.15075622 = sum of:
    0.016818931 = weight(_text_:of in 6006) [ClassicSimilarity], result of:
      0.016818931 = score(doc=6006,freq=26.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.31146988 = fieldWeight in 6006, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6006)
    0.12470941 = weight(_text_:innovations in 6006) [ClassicSimilarity], result of:
      0.12470941 = score(doc=6006,freq=4.0), product of:
        0.23478 = queryWeight, product of:
          6.7990475 = idf(docFreq=133, maxDocs=44218)
          0.034531306 = queryNorm
        0.5311756 = fieldWeight in 6006, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.7990475 = idf(docFreq=133, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6006)
    0.009227889 = weight(_text_:on in 6006) [ClassicSimilarity], result of:
      0.009227889 = score(doc=6006,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.121501654 = fieldWeight in 6006, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6006)
  0.27272728 = coord(3/11)

Abstract

Empirical evidence suggests that the ownership of related products that form a technology cluster is signifIcantly better than the attributes of an innovation at predicting adoption. The treatment of technology clusters, however, has been ad hoc and study specific: Researchers often make a priori assumptions about the relationships between technologies and measure ownership using lists of functionally related technology, without any systematic reasoning. Hence, the authors set out to examine empirically the composition of technology clusters and the differences, if any, in clusters of technologies formed by adopters and nonadopters. Using the Galileo system of multidimensional scaling and the associational diffusion framework, the dissimilarities between 30 technology concepts were scored by adopters and nonadopters. Results indicate clear differences in conceptualization of clusters: Adopters tend to relate technologies based an their functional similarity; here, innovations are perceived to be complementary, and hence, adoption of one technology spurs the adoption of related technologies. On the other hand, nonadopters tend to relate technologies using a stricter ascendancy of association where the adoption of an innovation makes subsequent innovations redundant. The results question the measurement approaches and present an alternative methodology.

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.11, S.1451-1460

0.030263776 = product of:
  0.110967174 = sum of:
    0.015315675 = weight(_text_:of in 3232) [ClassicSimilarity], result of:
      0.015315675 = score(doc=3232,freq=44.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.28363106 = fieldWeight in 3232, product of:
          6.6332498 = tf(freq=44.0), with freq of:
            44.0 = termFreq=44.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3232)
    0.05331421 = weight(_text_:technological in 3232) [ClassicSimilarity], result of:
      0.05331421 = score(doc=3232,freq=4.0), product of:
        0.18347798 = queryWeight, product of:
          5.3133807 = idf(docFreq=591, maxDocs=44218)
          0.034531306 = queryNorm
        0.2905755 = fieldWeight in 3232, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.3133807 = idf(docFreq=591, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3232)
    0.04233729 = weight(_text_:great in 3232) [ClassicSimilarity], result of:
      0.04233729 = score(doc=3232,freq=2.0), product of:
        0.19443816 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.034531306 = queryNorm
        0.21774168 = fieldWeight in 3232, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3232)
  0.27272728 = coord(3/11)

Abstract

Making the Nation Safer: The Role of Science and Technology in Countering Terrorism The commitment of the scientific, engineering, and health communities to helping the United States and the world respond to security challenges became evident after September 11, 2001. The U.S. National Research Council's report an "Making the Nation Safer: The Role of Science and Technology in Countering Terrorism," (National Research Council, 2002, p. 1) explains the context of such a new commitment: Terrorism is a serious threat to the Security of the United States and indeed the world. The vulnerability of societies to terrorist attacks results in part from the proliferation of chemical, biological, and nuclear weapons of mass destruction, but it also is a consequence of the highly efficient and interconnected systems that we rely an for key services such as transportation, information, energy, and health care. The efficient functioning of these systems reflects great technological achievements of the past century, but interconnectedness within and across systems also means that infrastructures are vulnerable to local disruptions, which could lead to widespread or catastrophic failures. As terrorists seek to exploit these vulnerabilities, it is fitting that we harness the nation's exceptional scientific and technological capabilities to Counter terrorist threats. A committee of 24 of the leading scientific, engineering, medical, and policy experts in the United States conducted the study described in the report. Eight panels were separately appointed and asked to provide input to the committee. The panels included: (a) biological sciences, (b) chemical issues, (c) nuclear and radiological issues, (d) information technology, (e) transportation, (f) energy facilities, Cities, and fixed infrastructure, (g) behavioral, social, and institutional issues, and (h) systems analysis and systems engineering. The focus of the committee's work was to make the nation safer from emerging terrorist threats that sought to inflict catastrophic damage an the nation's people, its infrastructure, or its economy. The committee considered nine areas, each of which is discussed in a separate chapter in the report: nuclear and radiological materials, human and agricultural health systems, toxic chemicals and explosive materials, information technology, energy systems, transportation systems, Cities and fixed infrastructure, the response of people to terrorism, and complex and interdependent systems. The chapter an information technology (IT) is particularly relevant to this special issue. The report recommends that "a strategic long-term research and development agenda should be established to address three primary counterterrorismrelated areas in IT: information and network security, the IT needs of emergency responders, and information fusion and management" (National Research Council, 2002, pp. 11 -12). The MD in information and network security should include approaches and architectures for prevention, identification, and containment of cyber-intrusions and recovery from them. The R&D to address IT needs of emergency responders should include ensuring interoperability, maintaining and expanding communications capability during an emergency, communicating with the public during an emergency, and providing support for decision makers. The R&D in information fusion and management for the intelligence, law enforcement, and emergency response communities should include data mining, data integration, language technologies, and processing of image and audio data. Much of the research reported in this special issue is related to information fusion and management for homeland security.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.217-220

0.030178603 = product of:
  0.110654876 = sum of:
    0.0130612515 = weight(_text_:of in 2657) [ClassicSimilarity], result of:
      0.0130612515 = score(doc=2657,freq=8.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.24188137 = fieldWeight in 2657, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
    0.012919044 = weight(_text_:on in 2657) [ClassicSimilarity], result of:
      0.012919044 = score(doc=2657,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.17010231 = fieldWeight in 2657, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
    0.08467458 = weight(_text_:great in 2657) [ClassicSimilarity], result of:
      0.08467458 = score(doc=2657,freq=2.0), product of:
        0.19443816 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.034531306 = queryNorm
        0.43548337 = fieldWeight in 2657, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2657)
  0.27272728 = coord(3/11)

Abstract

In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques

Source

Journal of the American Society for Information Science. 46(1995) no.3, S.194-216

0.029191284 = product of:
  0.107034706 = sum of:
    0.009695465 = weight(_text_:of in 275) [ClassicSimilarity], result of:
      0.009695465 = score(doc=275,freq=6.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.17955035 = fieldWeight in 275, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=275)
    0.024761025 = weight(_text_:on in 275) [ClassicSimilarity], result of:
      0.024761025 = score(doc=275,freq=10.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.32602316 = fieldWeight in 275, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=275)
    0.072578214 = weight(_text_:great in 275) [ClassicSimilarity], result of:
      0.072578214 = score(doc=275,freq=2.0), product of:
        0.19443816 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.034531306 = queryNorm
        0.37327147 = fieldWeight in 275, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.046875 = fieldNorm(doc=275)
  0.27272728 = coord(3/11)

Abstract

Link (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.842-855

0.02893219 = product of:
  0.10608469 = sum of:
    0.074435055 = weight(_text_:higher in 142) [ClassicSimilarity], result of:
      0.074435055 = score(doc=142,freq=4.0), product of:
        0.18138453 = queryWeight, product of:
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.034531306 = queryNorm
        0.41037157 = fieldWeight in 142, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
    0.013193856 = weight(_text_:of in 142) [ClassicSimilarity], result of:
      0.013193856 = score(doc=142,freq=16.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.24433708 = fieldWeight in 142, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
    0.018455777 = weight(_text_:on in 142) [ClassicSimilarity], result of:
      0.018455777 = score(doc=142,freq=8.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.24300331 = fieldWeight in 142, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=142)
  0.27272728 = coord(3/11)

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.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.3, S.351-365

0.027939547 = product of:
  0.102445 = sum of:
    0.07492303 = weight(_text_:effect in 1352) [ClassicSimilarity], result of:
      0.07492303 = score(doc=1352,freq=2.0), product of:
        0.18289955 = queryWeight, product of:
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.034531306 = queryNorm
        0.4096403 = fieldWeight in 1352, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1352)
    0.014602924 = weight(_text_:of in 1352) [ClassicSimilarity], result of:
      0.014602924 = score(doc=1352,freq=10.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.2704316 = fieldWeight in 1352, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1352)
    0.012919044 = weight(_text_:on in 1352) [ClassicSimilarity], result of:
      0.012919044 = score(doc=1352,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.17010231 = fieldWeight in 1352, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1352)
  0.27272728 = coord(3/11)

Abstract

We study the coupling of basic quantitative portfolio selection strategies with a financial news article prediction system, AZFinText. By varying the degrees of portfolio formation time, we found that a hybrid system using both quantitative strategy and a full set of financial news articles performed the best. With a 1-week portfolio formation period, we achieved a 20.79% trading return using a Momentum strategy and a 4.54% return using a Contrarian strategy over a 5-week holding period. We also found that trader overreaction to these events led AZFinText to capitalize on these short-term surges in price.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.2, S.247-255

0.026227767 = product of:
  0.09616847 = sum of:
    0.012516791 = weight(_text_:of in 1148) [ClassicSimilarity], result of:
      0.012516791 = score(doc=1148,freq=10.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.23179851 = fieldWeight in 1148, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
    0.011073467 = weight(_text_:on in 1148) [ClassicSimilarity], result of:
      0.011073467 = score(doc=1148,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.14580199 = fieldWeight in 1148, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
    0.072578214 = weight(_text_:great in 1148) [ClassicSimilarity], result of:
      0.072578214 = score(doc=1148,freq=2.0), product of:
        0.19443816 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.034531306 = queryNorm
        0.37327147 = fieldWeight in 1148, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.046875 = fieldNorm(doc=1148)
  0.27272728 = coord(3/11)

Abstract

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval

Source

Journal of the American Society for Information Science. 49(1998) no.8, S.693-705

0.023821225 = product of:
  0.08734449 = sum of:
    0.053516448 = weight(_text_:effect in 4468) [ClassicSimilarity], result of:
      0.053516448 = score(doc=4468,freq=2.0), product of:
        0.18289955 = queryWeight, product of:
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.034531306 = queryNorm
        0.2926002 = fieldWeight in 4468, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4468)
    0.013193856 = weight(_text_:of in 4468) [ClassicSimilarity], result of:
      0.013193856 = score(doc=4468,freq=16.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.24433708 = fieldWeight in 4468, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4468)
    0.02063419 = weight(_text_:on in 4468) [ClassicSimilarity], result of:
      0.02063419 = score(doc=4468,freq=10.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.271686 = fieldWeight in 4468, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4468)
  0.27272728 = coord(3/11)

Abstract

In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individual's impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1166-1176

0.02291468 = product of:
  0.08402049 = sum of:
    0.053516448 = weight(_text_:effect in 237) [ClassicSimilarity], result of:
      0.053516448 = score(doc=237,freq=2.0), product of:
        0.18289955 = queryWeight, product of:
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.034531306 = queryNorm
        0.2926002 = fieldWeight in 237, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.29663 = idf(docFreq=601, maxDocs=44218)
          0.0390625 = fieldNorm(doc=237)
    0.01745383 = weight(_text_:of in 237) [ClassicSimilarity], result of:
      0.01745383 = score(doc=237,freq=28.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.32322758 = fieldWeight in 237, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=237)
    0.013050207 = weight(_text_:on in 237) [ClassicSimilarity], result of:
      0.013050207 = score(doc=237,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.1718293 = fieldWeight in 237, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=237)
  0.27272728 = coord(3/11)

Abstract

We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.889-903

0.022133157 = product of:
  0.081154905 = sum of:
    0.05263353 = weight(_text_:higher in 6849) [ClassicSimilarity], result of:
      0.05263353 = score(doc=6849,freq=2.0), product of:
        0.18138453 = queryWeight, product of:
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.034531306 = queryNorm
        0.2901765 = fieldWeight in 6849, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6849)
    0.015471167 = weight(_text_:of in 6849) [ClassicSimilarity], result of:
      0.015471167 = score(doc=6849,freq=22.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.28651062 = fieldWeight in 6849, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6849)
    0.013050207 = weight(_text_:on in 6849) [ClassicSimilarity], result of:
      0.013050207 = score(doc=6849,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.1718293 = fieldWeight in 6849, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6849)
  0.27272728 = coord(3/11)

Abstract

It has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results from generic or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta-Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.

Source

Journal of the American Society for Information Science and technology. 52(2001) no.13, S.1134-1147

0.02109763 = product of:
  0.07735798 = sum of:
    0.017584248 = weight(_text_:of in 5460) [ClassicSimilarity], result of:
      0.017584248 = score(doc=5460,freq=58.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.32564276 = fieldWeight in 5460, product of:
          7.615773 = tf(freq=58.0), with freq of:
            58.0 = termFreq=58.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5460)
    0.05331421 = weight(_text_:technological in 5460) [ClassicSimilarity], result of:
      0.05331421 = score(doc=5460,freq=4.0), product of:
        0.18347798 = queryWeight, product of:
          5.3133807 = idf(docFreq=591, maxDocs=44218)
          0.034531306 = queryNorm
        0.2905755 = fieldWeight in 5460, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.3133807 = idf(docFreq=591, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5460)
    0.006459522 = weight(_text_:on in 5460) [ClassicSimilarity], result of:
      0.006459522 = score(doc=5460,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.08505116 = fieldWeight in 5460, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5460)
  0.27272728 = coord(3/11)

Abstract

The purpose of this paper is to identify and explain the role of individual learning and development in acquiring tacit knowledge in the context of the inexorable and intense continuous change (technological and otherwise) that characterizes our society today, and also to investigate the software (SW) sector, which is at the core of contemporary continuous change and is a paradigm of effective and intrinsic knowledge sharing (KS). This makes the SW sector unique and different from others where KS is so hard to implement. Design/methodology/approach The study employed an inductive qualitative approach based on a multi-case study approach, composed of three successful SW companies in China. These companies are representative of the fabric of the sector, namely a small- and medium-sized enterprise, a large private company and a large state-owned enterprise. The fieldwork included 44 participants who were interviewed using a semi-structured script. The interview data were coded and interpreted following the Straussian grounded theory pattern of open coding, axial coding and selective coding. The process of interviewing was stopped when theoretical saturation was achieved after a careful process of theoretical sampling.
Findings The findings of this research suggest that individual learning and development are deemed to be the fundamental feature for professional success and survival in the continuously changing environment of the SW industry today. However, individual learning was described by the participants as much more than a mere individual process. It involves a collective and participatory effort within the organization and the sector as a whole, and a KS process that transcends organizational, cultural and national borders. Individuals in particular are mostly motivated by the pressing need to face and adapt to the dynamic and changeable environments of today's digital society that is led by the sector. Software practitioners are continuously in need of learning, refreshing and accumulating tacit knowledge, partly because it is required by their companies, but also due to a sound awareness of continuous technical and technological changes that seem only to increase with the advances of information technology. This led to a clear theoretical understanding that the continuous change that faces the sector has led to individual acquisition of culture and somatic knowledge that in turn lay the foundation for not only the awareness of the need for continuous individual professional development but also for the creation of habitus related to KS and continuous learning. Originality/value The study reported in this paper shows that there is a theoretical link between the existence of conducive organizational and sector-wide somatic and cultural knowledge, and the success of KS practices that lead to individual learning and development. Therefore, the theory proposed suggests that somatic and cultural knowledge are crucial drivers for the creation of habitus of individual tacit knowledge acquisition. The paper further proposes a habitus-driven individual development (HDID) Theoretical Model that can be of use to both academics and practitioners interested in fostering and developing processes of KS and individual development in knowledge-intensive organizations.

Source

Journal of documentation. 75(2019) no.5, S.927-953

0.020758472 = product of:
  0.076114394 = sum of:
    0.05263353 = weight(_text_:higher in 871) [ClassicSimilarity], result of:
      0.05263353 = score(doc=871,freq=2.0), product of:
        0.18138453 = queryWeight, product of:
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.034531306 = queryNorm
        0.2901765 = fieldWeight in 871, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.252756 = idf(docFreq=628, maxDocs=44218)
          0.0390625 = fieldNorm(doc=871)
    0.010430659 = weight(_text_:of in 871) [ClassicSimilarity], result of:
      0.010430659 = score(doc=871,freq=10.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.19316542 = fieldWeight in 871, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=871)
    0.013050207 = weight(_text_:on in 871) [ClassicSimilarity], result of:
      0.013050207 = score(doc=871,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.1718293 = fieldWeight in 871, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=871)
  0.27272728 = coord(3/11)

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

Source

Journal of the American Society for Information Science. 49(1998) no.7, S.604-618

0.012416842 = product of:
  0.04552842 = sum of:
    0.015832627 = weight(_text_:of in 2733) [ClassicSimilarity], result of:
      0.015832627 = score(doc=2733,freq=16.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.2932045 = fieldWeight in 2733, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.015660247 = weight(_text_:on in 2733) [ClassicSimilarity], result of:
      0.015660247 = score(doc=2733,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.20619515 = fieldWeight in 2733, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
    0.014035545 = product of:
      0.02807109 = sum of:
        0.02807109 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.02807109 = score(doc=2733,freq=2.0), product of:
            0.12092275 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.034531306 = 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)
      0.5 = coord(1/2)
  0.27272728 = coord(3/11)

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

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607

0.011156074 = product of:
  0.040905602 = sum of:
    0.016159108 = weight(_text_:of in 2753) [ClassicSimilarity], result of:
      0.016159108 = score(doc=2753,freq=24.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.2992506 = fieldWeight in 2753, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2753)
    0.013050207 = weight(_text_:on in 2753) [ClassicSimilarity], result of:
      0.013050207 = score(doc=2753,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.1718293 = fieldWeight in 2753, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2753)
    0.011696288 = product of:
      0.023392577 = sum of:
        0.023392577 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
          0.023392577 = score(doc=2753,freq=2.0), product of:
            0.12092275 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.034531306 = queryNorm
            0.19345059 = fieldWeight in 2753, 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=2753)
      0.5 = coord(1/2)
  0.27272728 = coord(3/11)

Abstract

Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the link-formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social-network analysis methods to examine several plausible facilitators of co-offending relationships in a large-scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two-proportion z-test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co-offending. These findings provide important insights into the link-formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.

Date

22. 3.2009 18:50:30

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.655-665

0.010914868 = product of:
  0.04002118 = sum of:
    0.012341722 = weight(_text_:of in 7469) [ClassicSimilarity], result of:
      0.012341722 = score(doc=7469,freq=14.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.22855641 = fieldWeight in 7469, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.015983174 = weight(_text_:on in 7469) [ClassicSimilarity], result of:
      0.015983174 = score(doc=7469,freq=6.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.21044704 = fieldWeight in 7469, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=7469)
    0.011696288 = product of:
      0.023392577 = sum of:
        0.023392577 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.023392577 = score(doc=7469,freq=2.0), product of:
            0.12092275 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.034531306 = queryNorm
            0.19345059 = fieldWeight in 7469, 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=7469)
      0.5 = coord(1/2)
  0.27272728 = coord(3/11)

Abstract

With the growth of hypertext and multimedia applications that support and encourage browsing it is time to take a penetrating look at browsing behaviour. Several dimensions of browsing are exemined, to find out: first, what is browsing and what cognitive processes are associated with it: second, is there a browsing strategy, and if so, are there any differences between how subject-area experts and novices browse; and finally, how can this knowledge be applied to improve the design of hypertext systems. Two groups of students, subject-area experts and novices, were studied while browsing a Macintosh HyperCard application on the subject The Vietnam War. A protocol analysis technique was used to gather and analyze data. Components of the GOMS model were used to describe the goals, operators, methods, and selection rules observed: Three browsing strategies were identified: (1) search-oriented browse, scanning and and reviewing information relevant to a fixed task; (2) review-browse, scanning and reviewing intersting information in the presence of transient browse goals that represent changing tasks, and (3) scan-browse, scanning for interesting information (without review). Most subjects primarily used review-browse interspersed with search-oriented browse. Within this strategy, comparisons between subject-area experts and novices revealed differences in tactics: experts browsed in more depth, seldom used referential links, selected different kinds of topics, and viewed information differently thatn did novices. Based on these findings, suggestions are made to hypertext developers

Source

IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884

0.010795394 = product of:
  0.03958311 = sum of:
    0.01865893 = weight(_text_:of in 5276) [ClassicSimilarity], result of:
      0.01865893 = score(doc=5276,freq=32.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.34554482 = fieldWeight in 5276, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.009227889 = weight(_text_:on in 5276) [ClassicSimilarity], result of:
      0.009227889 = score(doc=5276,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.121501654 = fieldWeight in 5276, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5276)
    0.011696288 = product of:
      0.023392577 = sum of:
        0.023392577 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.023392577 = score(doc=5276,freq=2.0), product of:
            0.12092275 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.034531306 = queryNorm
            0.19345059 = fieldWeight in 5276, 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=5276)
      0.5 = coord(1/2)
  0.27272728 = coord(3/11)

Abstract

With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.

Date

22. 7.2006 16:14:37

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.378-393

0.010114968 = product of:
  0.037088215 = sum of:
    0.012341722 = weight(_text_:of in 5259) [ClassicSimilarity], result of:
      0.012341722 = score(doc=5259,freq=14.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.22855641 = fieldWeight in 5259, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5259)
    0.013050207 = weight(_text_:on in 5259) [ClassicSimilarity], result of:
      0.013050207 = score(doc=5259,freq=4.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.1718293 = fieldWeight in 5259, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5259)
    0.011696288 = product of:
      0.023392577 = sum of:
        0.023392577 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.023392577 = score(doc=5259,freq=2.0), product of:
            0.12092275 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.034531306 = queryNorm
            0.19345059 = fieldWeight in 5259, 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=5259)
      0.5 = coord(1/2)
  0.27272728 = coord(3/11)

Abstract

The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%).

Date

22. 7.2006 14:26:01

Footnote

Beitrag in einem special issue on bioinformatics

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.5, S.457-468

0.006905375 = product of:
  0.03797956 = sum of:
    0.015832627 = weight(_text_:of in 492) [ClassicSimilarity], result of:
      0.015832627 = score(doc=492,freq=4.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.2932045 = fieldWeight in 492, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
    0.022146935 = weight(_text_:on in 492) [ClassicSimilarity], result of:
      0.022146935 = score(doc=492,freq=2.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.29160398 = fieldWeight in 492, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.09375 = fieldNorm(doc=492)
  0.18181819 = coord(2/11)

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

0.006705677 = product of:
  0.036881223 = sum of:
    0.017701415 = weight(_text_:of in 1880) [ClassicSimilarity], result of:
      0.017701415 = score(doc=1880,freq=20.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.32781258 = fieldWeight in 1880, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1880)
    0.01917981 = weight(_text_:on in 1880) [ClassicSimilarity], result of:
      0.01917981 = score(doc=1880,freq=6.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.25253648 = fieldWeight in 1880, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=1880)
  0.18181819 = coord(2/11)

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.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1347-1359

0.006519708 = product of:
  0.035858393 = sum of:
    0.013711456 = weight(_text_:of in 2203) [ClassicSimilarity], result of:
      0.013711456 = score(doc=2203,freq=12.0), product of:
        0.053998582 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.034531306 = queryNorm
        0.25392252 = fieldWeight in 2203, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
    0.022146935 = weight(_text_:on in 2203) [ClassicSimilarity], result of:
      0.022146935 = score(doc=2203,freq=8.0), product of:
        0.07594867 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.034531306 = queryNorm
        0.29160398 = fieldWeight in 2203, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2203)
  0.18181819 = coord(2/11)

Abstract

Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies

Source

Journal of the American Society for Information Science. 46(1995) no.5, S.348-369