Search (15 results, page 1 of 1)

0.028620303 = product of:
  0.057240605 = sum of:
    0.057240605 = product of:
      0.11448121 = sum of:
        0.11448121 = weight(_text_:searching in 2393) [ClassicSimilarity], result of:
          0.11448121 = score(doc=2393,freq=12.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.5473945 = fieldWeight in 2393, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.022896243 = product of:
  0.045792487 = sum of:
    0.045792487 = product of:
      0.09158497 = sum of:
        0.09158497 = weight(_text_:searching in 869) [ClassicSimilarity], result of:
          0.09158497 = score(doc=869,freq=12.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.43791562 = fieldWeight in 869, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.03125 = fieldNorm(doc=869)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Internet provides an exceptional testbed for developing algorithms that can improve bowsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and / or searching the Internet. Our results indicate that a Kohonen self-organizing map (SOM)-based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment subcategory of Yahoo!) into manageable sub-spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the work did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject-suggested terms, thesaurus-suggested terms, and the combination of subject- and thesaurus-suggested terms. The recall measures indicated that the combination of subject- and thesaurs-suggested terms exhibited significantly better recall than subject-suggested terms alone. Furthermore, analysis of the homepages indicated that there was limited overlap between the homepages retrieved by the subject-suggested and thesaurus-suggested terms. Since the retrieval homepages for the most part were different, this suggests that a user can enhance a keyword-based search by using an automatically generated concept space. Subejcts especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were 'real' (i.e., orininating in the homepages) and therefore guaranteed to have retrieval success

0.01652394 = product of:
  0.03304788 = sum of:
    0.03304788 = product of:
      0.06609576 = sum of:
        0.06609576 = weight(_text_:searching in 6849) [ClassicSimilarity], result of:
          0.06609576 = score(doc=6849,freq=4.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.3160384 = fieldWeight in 6849, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6849)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.01652394 = product of:
  0.03304788 = sum of:
    0.03304788 = product of:
      0.06609576 = sum of:
        0.06609576 = weight(_text_:searching in 1615) [ClassicSimilarity], result of:
          0.06609576 = score(doc=1615,freq=4.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.3160384 = fieldWeight in 1615, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1615)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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

0.01652394 = product of:
  0.03304788 = sum of:
    0.03304788 = product of:
      0.06609576 = sum of:
        0.06609576 = weight(_text_:searching in 142) [ClassicSimilarity], result of:
          0.06609576 = score(doc=142,freq=4.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.3160384 = fieldWeight in 142, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=142)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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

0.014021028 = product of:
  0.028042056 = sum of:
    0.028042056 = product of:
      0.05608411 = sum of:
        0.05608411 = weight(_text_:searching in 5704) [ClassicSimilarity], result of:
          0.05608411 = score(doc=5704,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.26816747 = fieldWeight in 5704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.014021028 = product of:
  0.028042056 = sum of:
    0.028042056 = product of:
      0.05608411 = sum of:
        0.05608411 = weight(_text_:searching in 4242) [ClassicSimilarity], result of:
          0.05608411 = score(doc=4242,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.26816747 = fieldWeight in 4242, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.046875 = fieldNorm(doc=4242)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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

0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 871) [ClassicSimilarity], result of:
          0.04673676 = score(doc=871,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 871, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=871)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 2505) [ClassicSimilarity], result of:
          0.04673676 = score(doc=2505,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 2505, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2505)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

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

0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 3452) [ClassicSimilarity], result of:
          0.04673676 = score(doc=3452,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 3452, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

0.010506795 = product of:
  0.02101359 = sum of:
    0.02101359 = product of:
      0.04202718 = sum of:
        0.04202718 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.04202718 = score(doc=2733,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = 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)
  0.5 = coord(1/2)

Date

22. 3.2009 17:57:50

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.035022654 = score(doc=7469,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = 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)
  0.5 = coord(1/2)

Source

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

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.035022654 = score(doc=5259,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = 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)
  0.5 = coord(1/2)

Date

22. 7.2006 14:26:01

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.035022654 = score(doc=5276,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = 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)
  0.5 = coord(1/2)

Date

22. 7.2006 16:14:37

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
          0.035022654 = score(doc=2753,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = 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)
  0.5 = coord(1/2)

Date

22. 3.2009 18:50:30