Search (4 results, page 1 of 1)

0.0062585147 = product of:
  0.025034059 = sum of:
    0.025034059 = product of:
      0.050068118 = sum of:
        0.050068118 = weight(_text_:organization in 1367) [ClassicSimilarity], result of:
          0.050068118 = score(doc=1367,freq=4.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.27854347 = fieldWeight in 1367, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1367)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

VDL (Visual Distinctive Language)-based iconic know ledge tags are graphically structured icons for knowledge representation. VDL was developed and assessed to enhance the connection of iconic tags and the connection of tagged knowledge. The purpose of this paper is to present further investigation on an arrangement method for these special tags as well as the characteristics of better tag presentation in knowledge organization systems (KOS). An online experiment was conducted to compare tagging results of four types of iconic tag presentations: two types of iconic tags (VDL-based iconic tags and iconic tags without explicit structure) under two arrangement methods respectively (random arrangement and semantic arrangement). Tagging quality and tagging speed were measured to identify how users locate and locate again appropriate iconic tags for knowledge tagging. A supplementary test on tag structure identification was also carried out for each tag presentation. Semantic arrangement of VDL-based icons helped users to tag given articles with more appropriate tags in less time. Users identified better tag structure in this type of tag presentation. This in-depth work of VDL-based iconic tags is among the first to investigate how to visually structure knowledge tags, a problem neglected by previous studies on icon knowledge representation.

Source

Knowledge organization. 41(2014) no.1, S.14-29

0.0030039945 = product of:
  0.012015978 = sum of:
    0.012015978 = weight(_text_:information in 62) [ClassicSimilarity], result of:
      0.012015978 = score(doc=62,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13576832 = fieldWeight in 62, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=62)
  0.25 = coord(1/4)

Abstract

The paper discusses the challenges of categorising documents and "items of the world" to promote knowledge sharing in large communities of interest. We present the DOCMA method (Document and Item-based Model for Action) dedicated to end-users who have minimal or no knowledge of information science. Community members can elicit structure and indexed business items stemming from their query including projects, actors, products, places of interest, and geo-situated objects. This hybrid method was applied in a collaborative Web portal in the field of sustainability for the past two years.

0.0030039945 = product of:
  0.012015978 = sum of:
    0.012015978 = weight(_text_:information in 4836) [ClassicSimilarity], result of:
      0.012015978 = score(doc=4836,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13576832 = fieldWeight in 4836, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4836)
  0.25 = coord(1/4)

Abstract

We present the DOCMA method (DOCument and Model for Action) focused to Socio-Semantic web applications in large communities of interest. DOCMA is dedicated to end-users without any knowledge in Information Science. Community Members can elicit, structure and index shared business items emerging from their inquiry (such as projects, actors, products, geographically situated objects of interest.). We apply DOCMA to an experiment in the field of Sustainable Development: the Cartodd-Map21 collaborative Web portal.

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 4837) [ClassicSimilarity], result of:
      0.008582841 = score(doc=4837,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 4837, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4837)
  0.25 = coord(1/4)

Abstract

Icon system could represent an efficient solution for collective iconic categorization of knowledge by providing graphical interpretation. Their pictorial characters assist visualizing the structure of text to become more understandable beyond vocabulary obstacle. In this paper we are proposing a Knowledge Engineering (KM) based iconic representation approach. We assume that these systematic icons improve collective knowledge management. Meanwhile, text (constructed under our knowledge management model - Hypertopic) helps to reduce the diversity of graphical understanding belonging to different users. This "position paper" also prepares to demonstrate our hypothesis by an "iconic social tagging" experiment which is to be accomplished in 2011 with UTT students. We describe the "socio semantic web" information portal involved in this project, and a part of the icons already designed for this experiment in Sustainability field. We have reviewed existing theoretical works on icons from various origins, which can be used to lay the foundation of robust "icons systems".