Search (2 results, page 1 of 1)

  • × author_ss:"Zhang, J."
  • × theme_ss:"Visualisierung"
  1. Zhang, J.; Zhao, Y.: ¬A user term visualization analysis based on a social question and answer log (2013) 0.01 
    0.011243585 = product of:
  0.11243584 = sum of:
    0.11243584 = weight(_text_:log in 2715) [ClassicSimilarity], result of:
      0.11243584 = score(doc=2715,freq=6.0), product of:
        0.18335998 = queryWeight, product of:
          6.4086204 = idf(docFreq=197, maxDocs=44218)
          0.028611459 = queryNorm
        0.61319727 = fieldWeight in 2715, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          6.4086204 = idf(docFreq=197, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2715)
  0.1 = coord(1/10)
    Abstract
    The authors of this paper investigate terms of consumers' diabetes based on a log from the Yahoo!Answers social question and answers (Q&A) forum, ascertain characteristics and relationships among terms related to diabetes from the consumers' perspective, and reveal users' diabetes information seeking patterns. In this study, the log analysis method, data coding method, and visualization multiple-dimensional scaling analysis method were used for analysis. The visual analyses were conducted at two levels: terms analysis within a category and category analysis among the categories in the schema. The findings show that the average number of words per question was 128.63, the average number of sentences per question was 8.23, the average number of words per response was 254.83, and the average number of sentences per response was 16.01. There were 12 categories (Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Education & Info Resource, Affect, Social & Culture, Lifestyle, and Nutrient) in the diabetes related schema which emerged from the data coding analysis. The analyses at the two levels show that terms and categories were clustered and patterns were revealed. Future research directions are also included.
  2. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00 
    8.4169564E-4 = product of:
  0.008416956 = sum of:
    0.008416956 = weight(_text_:web in 1211) [ClassicSimilarity], result of:
      0.008416956 = score(doc=1211,freq=2.0), product of:
        0.0933738 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.028611459 = queryNorm
        0.09014259 = fieldWeight in 1211, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.1 = coord(1/10)
    Abstract
    In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.

Authors

Years

Types