Search (61 results, page 1 of 4)

0.029173631 = product of:
  0.116694525 = sum of:
    0.116694525 = sum of:
      0.00863584 = weight(_text_:a in 5276) [ClassicSimilarity], result of:
        0.00863584 = score(doc=5276,freq=12.0), product of:
          0.055348642 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.04800207 = queryNorm
          0.15602624 = fieldWeight in 5276, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5276)
      0.075540595 = weight(_text_:z in 5276) [ClassicSimilarity], result of:
        0.075540595 = score(doc=5276,freq=2.0), product of:
          0.2562021 = queryWeight, product of:
            5.337313 = idf(docFreq=577, maxDocs=44218)
            0.04800207 = queryNorm
          0.29484767 = fieldWeight in 5276, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.337313 = idf(docFreq=577, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5276)
      0.032518093 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
        0.032518093 = score(doc=5276,freq=2.0), product of:
          0.16809508 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04800207 = 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)
  0.25 = coord(1/4)

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

Type

a

0.028985523 = product of:
  0.11594209 = sum of:
    0.11594209 = sum of:
      0.007883408 = weight(_text_:a in 2753) [ClassicSimilarity], result of:
        0.007883408 = score(doc=2753,freq=10.0), product of:
          0.055348642 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.04800207 = queryNorm
          0.14243183 = fieldWeight in 2753, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2753)
      0.075540595 = weight(_text_:z in 2753) [ClassicSimilarity], result of:
        0.075540595 = score(doc=2753,freq=2.0), product of:
          0.2562021 = queryWeight, product of:
            5.337313 = idf(docFreq=577, maxDocs=44218)
            0.04800207 = queryNorm
          0.29484767 = fieldWeight in 2753, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.337313 = idf(docFreq=577, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2753)
      0.032518093 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
        0.032518093 = score(doc=2753,freq=2.0), product of:
          0.16809508 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04800207 = 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)
  0.25 = coord(1/4)

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

Type

a

0.016835287 = product of:
  0.06734115 = sum of:
    0.06734115 = product of:
      0.10101172 = sum of:
        0.010363008 = weight(_text_:a in 1611) [ClassicSimilarity], result of:
          0.010363008 = score(doc=1611,freq=12.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.18723148 = fieldWeight in 1611, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1611)
        0.09064872 = weight(_text_:z in 1611) [ClassicSimilarity], result of:
          0.09064872 = score(doc=1611,freq=2.0), product of:
            0.2562021 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04800207 = queryNorm
            0.35381722 = fieldWeight in 1611, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.046875 = fieldNorm(doc=1611)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

Abstract

Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.

Type

a

0.016684802 = product of:
  0.06673921 = sum of:
    0.06673921 = product of:
      0.10010881 = sum of:
        0.00946009 = weight(_text_:a in 501) [ClassicSimilarity], result of:
          0.00946009 = score(doc=501,freq=10.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.1709182 = fieldWeight in 501, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=501)
        0.09064872 = weight(_text_:z in 501) [ClassicSimilarity], result of:
          0.09064872 = score(doc=501,freq=2.0), product of:
            0.2562021 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04800207 = queryNorm
            0.35381722 = fieldWeight in 501, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.046875 = fieldNorm(doc=501)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

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.

Type

a

0.0142520685 = product of:
  0.057008274 = sum of:
    0.057008274 = product of:
      0.08551241 = sum of:
        0.0099718105 = weight(_text_:a in 2393) [ClassicSimilarity], result of:
          0.0099718105 = score(doc=2393,freq=16.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.18016359 = fieldWeight in 2393, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
        0.075540595 = weight(_text_:z in 2393) [ClassicSimilarity], result of:
          0.075540595 = score(doc=2393,freq=2.0), product of:
            0.2562021 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04800207 = queryNorm
            0.29484767 = fieldWeight in 2393, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

Abstract

The rapid growth of the non-English-speaking Internet population has created a need for better searching and browsing capabilities in languages other than English. However, existing search engines may not serve the needs of many non-English-speaking Internet users. In this paper, we propose a generic and integrated approach to searching and browsing the Internet in a multilingual world. Based an this approach, we have developed the Chinese Business Intelligence Portal (CBizPort), a meta-search engine that searches for business information of mainland China, Taiwan, and Hong Kong. Additional functions provided by CBizPort include encoding conversion (between Simplified Chinese and Traditional Chinese), summarization, and categorization. Experimental results of our user evaluation study show that the searching and browsing performance of CBizPort was comparable to that of regional Chinese search engines, and CBizPort could significantly augment these search engines. Subjects' verbal comments indicate that CBizPort performed best in terms of analysis functions, cross-regional searching, and user-friendliness, whereas regional search engines were more efficient and more popular. Subjects especially liked CBizPort's summarizer and categorizer, which helped in understanding search results. These encouraging results suggest a promising future of our approach to Internet searching and browsing in a multilingual world.

Type

a

0.0137652885 = product of:
  0.055061154 = sum of:
    0.055061154 = product of:
      0.08259173 = sum of:
        0.007051134 = weight(_text_:a in 4367) [ClassicSimilarity], result of:
          0.007051134 = score(doc=4367,freq=8.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.12739488 = fieldWeight in 4367, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4367)
        0.075540595 = weight(_text_:z in 4367) [ClassicSimilarity], result of:
          0.075540595 = score(doc=4367,freq=2.0), product of:
            0.2562021 = queryWeight, product of:
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.04800207 = queryNorm
            0.29484767 = fieldWeight in 4367, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.337313 = idf(docFreq=577, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4367)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

Abstract

Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.

Type

a

0.008369173 = product of:
  0.03347669 = sum of:
    0.03347669 = product of:
      0.050215036 = sum of:
        0.0111933295 = weight(_text_:a in 2733) [ClassicSimilarity], result of:
          0.0111933295 = score(doc=2733,freq=14.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20223314 = fieldWeight in 2733, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2733)
        0.039021708 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.039021708 = score(doc=2733,freq=2.0), product of:
            0.16809508 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04800207 = 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)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

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

Type

a

0.006974311 = product of:
  0.027897244 = sum of:
    0.027897244 = product of:
      0.041845866 = sum of:
        0.009327774 = weight(_text_:a in 7469) [ClassicSimilarity], result of:
          0.009327774 = score(doc=7469,freq=14.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.1685276 = fieldWeight in 7469, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=7469)
        0.032518093 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.032518093 = score(doc=7469,freq=2.0), product of:
            0.16809508 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04800207 = 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)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

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

Type

a

0.0067335833 = product of:
  0.026934333 = sum of:
    0.026934333 = product of:
      0.0404015 = sum of:
        0.007883408 = weight(_text_:a in 5259) [ClassicSimilarity], result of:
          0.007883408 = score(doc=5259,freq=10.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.14243183 = fieldWeight in 5259, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5259)
        0.032518093 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.032518093 = score(doc=5259,freq=2.0), product of:
            0.16809508 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04800207 = 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)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

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

Type

a

0.0013641815 = product of:
  0.005456726 = sum of:
    0.005456726 = product of:
      0.016370177 = sum of:
        0.016370177 = weight(_text_:a in 1352) [ClassicSimilarity], result of:
          0.016370177 = score(doc=1352,freq=22.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.29576474 = fieldWeight in 1352, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1352)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

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.

Type

a

9.97181E-4 = product of:
  0.003988724 = sum of:
    0.003988724 = product of:
      0.011966172 = sum of:
        0.011966172 = weight(_text_:a in 2203) [ClassicSimilarity], result of:
          0.011966172 = score(doc=2203,freq=16.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.2161963 = fieldWeight in 2203, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2203)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

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

Type

a

9.401512E-4 = product of:
  0.003760605 = sum of:
    0.003760605 = product of:
      0.011281814 = sum of:
        0.011281814 = weight(_text_:a in 3845) [ClassicSimilarity], result of:
          0.011281814 = score(doc=3845,freq=8.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20383182 = fieldWeight in 3845, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=3845)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

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

Type

a

9.401512E-4 = product of:
  0.003760605 = sum of:
    0.003760605 = product of:
      0.011281814 = sum of:
        0.011281814 = weight(_text_:a in 5283) [ClassicSimilarity], result of:
          0.011281814 = score(doc=5283,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20383182 = fieldWeight in 5283, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.125 = fieldNorm(doc=5283)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

9.401512E-4 = product of:
  0.003760605 = sum of:
    0.003760605 = product of:
      0.011281814 = sum of:
        0.011281814 = weight(_text_:a in 7623) [ClassicSimilarity], result of:
          0.011281814 = score(doc=7623,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20383182 = fieldWeight in 7623, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.125 = fieldNorm(doc=7623)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

9.401512E-4 = product of:
  0.003760605 = sum of:
    0.003760605 = product of:
      0.011281814 = sum of:
        0.011281814 = weight(_text_:a in 8221) [ClassicSimilarity], result of:
          0.011281814 = score(doc=8221,freq=8.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20383182 = fieldWeight in 8221, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=8221)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

Attempts to identify the nature and causes of information management indeterminism in an online research environment and proposes solutions for alleviating this indeterminism. Conducts two empirical studies of information management activities. The first identified the types and nature of information management indeterminism by evaluating archived text. The second focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. Proposes a knowledge based design for alleviating indeterminism, which contains a system generated thesaurus and an inferencing engine

Type

a

9.401512E-4 = product of:
  0.003760605 = sum of:
    0.003760605 = product of:
      0.011281814 = sum of:
        0.011281814 = weight(_text_:a in 8549) [ClassicSimilarity], result of:
          0.011281814 = score(doc=8549,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.20383182 = fieldWeight in 8549, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.125 = fieldNorm(doc=8549)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

9.19731E-4 = product of:
  0.003678924 = sum of:
    0.003678924 = product of:
      0.011036771 = sum of:
        0.011036771 = weight(_text_:a in 6928) [ClassicSimilarity], result of:
          0.011036771 = score(doc=6928,freq=10.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.19940455 = fieldWeight in 6928, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6928)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

Describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Describes an electronic meeting system and describes the classification problem that exists in the group problem solving process. Surveys the literature concerning classification. Describes the application of the Kohonen SOM to the meeting output classification problem. Describes an experiment that evaluated the classification performed by the Kohonen SOM by comparing it with those of a human expert and a Hopfield neural network. Discusses conclusions and directions for future research

Type

a

8.9644286E-4 = product of:
  0.0035857714 = sum of:
    0.0035857714 = product of:
      0.010757314 = sum of:
        0.010757314 = weight(_text_:a in 4276) [ClassicSimilarity], result of:
          0.010757314 = score(doc=4276,freq=38.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.19435552 = fieldWeight in 4276, product of:
              6.164414 = tf(freq=38.0), with freq of:
                38.0 = termFreq=38.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4276)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

Type

a

8.813918E-4 = product of:
  0.0035255672 = sum of:
    0.0035255672 = product of:
      0.010576702 = sum of:
        0.010576702 = weight(_text_:a in 6492) [ClassicSimilarity], result of:
          0.010576702 = score(doc=6492,freq=18.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.19109234 = fieldWeight in 6492, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6492)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

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

Type

a

8.63584E-4 = product of:
  0.003454336 = sum of:
    0.003454336 = product of:
      0.010363008 = sum of:
        0.010363008 = weight(_text_:a in 5202) [ClassicSimilarity], result of:
          0.010363008 = score(doc=5202,freq=12.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.18723148 = fieldWeight in 5202, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=5202)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

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

Type

a