Search (2 results, page 1 of 1)

  • × author_ss:"Bagchi, M."
  1. Das, S.; Bagchi, M.; Hussey, P.: How to teach domain ontology-based knowledge graph construction? : an Irish experiment (2023) 0.02 
    0.018982807 = product of:
  0.037965614 = sum of:
    0.037965614 = sum of:
      0.006765375 = weight(_text_:a in 1126) [ClassicSimilarity], result of:
        0.006765375 = score(doc=1126,freq=8.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.12739488 = fieldWeight in 1126, 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=1126)
      0.03120024 = weight(_text_:22 in 1126) [ClassicSimilarity], result of:
        0.03120024 = score(doc=1126,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.19345059 = fieldWeight in 1126, 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=1126)
  0.5 = coord(1/2)
    Abstract
    Domains represent concepts which belong to specific parts of the world. The particularized meaning of words linguistically encoding such domain concepts are provided by domain specific resources. The explicit meaning of such words are increasingly captured computationally using domain-specific ontologies, which, even for the same reference domain, are most often than not semantically incompatible. As information systems that rely on domain ontologies expand, there is a growing need to not only design domain ontologies and domain ontology-grounded Knowl­edge Graphs (KGs) but also to align them to general standards and conventions for interoperability. This often presents an insurmountable challenge to domain experts who have to additionally learn the construction of domain ontologies and KGs. Until now, several research methodologies have been proposed by different research groups using different technical approaches and based on scenarios of different domains of application. However, no methodology has been proposed which not only facilitates designing conceptually well-founded ontologies, but is also, equally, grounded in the general pedagogical principles of knowl­edge organization and, thereby, flexible enough to teach, and reproduce vis-à-vis domain experts. The purpose of this paper is to provide such a general, pedagogically flexible semantic knowl­edge modelling methodology. We exemplify the methodology by examples and illustrations from a professional-level digital healthcare course, and conclude with an evaluation grounded in technological parameters as well as user experience design principles.
    Date
    20.11.2023 17:19:22
    Type
    a
  2. Bagchi, M.: ¬A large-scale, knowledge-intensive domain-development methodology (2021) 0.00 
    0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 438) [ClassicSimilarity], result of:
          0.008118451 = score(doc=438,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 438, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=438)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Since time immemorial, organization and visualization has emerged as the pre-eminent natural combination through which abstract concepts in a domain can be understood, imbibed and communicated. In the present era of big data and information explosion, domains are becoming increasingly intricate and facetized, often leaving traditional approaches of knowledge organization functionally inefficient in dynamically depicting intellectual landscapes. The paper attempts to present, ab initio, a step-by-step conceptual domain development methodology using knowledge graphs, rooted in the rudiments of interdisciplinary knowledge organization and knowledge cartography. It briefly highlights the implementation of the proposed methodology on business domain data, and considers its research ramifications, originality and limitations from multiple perspectives. The paper concludes by summarizing observations on the entire work and particularizing future lines of research.
    Type
    a

Authors