Search (2 results, page 1 of 1)

0.009469198 = product of:
  0.018938396 = sum of:
    0.018938396 = product of:
      0.037876792 = sum of:
        0.037876792 = weight(_text_:b in 3867) [ClassicSimilarity], result of:
          0.037876792 = score(doc=3867,freq=2.0), product of:
            0.16126883 = queryWeight, product of:
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.045518078 = queryNorm
            0.23486741 = fieldWeight in 3867, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.046875 = fieldNorm(doc=3867)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.

0.007890998 = product of:
  0.015781997 = sum of:
    0.015781997 = product of:
      0.031563994 = sum of:
        0.031563994 = weight(_text_:b in 3228) [ClassicSimilarity], result of:
          0.031563994 = score(doc=3228,freq=2.0), product of:
            0.16126883 = queryWeight, product of:
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.045518078 = queryNorm
            0.19572285 = fieldWeight in 3228, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3228)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

F1000 recommendations were assessed as a potential data source for research evaluation, but the reasons for differences between F1000 Article Factor (FFa scores) and citations remain unexplored. By linking recommendations for 28,254 publications in F1000 with citations in Scopus, we investigated the effect of research level (basic, clinical, mixed) and article type on the internal consistency of assessments based on citations and FFa scores. The research level has little impact on the differences between the 2 evaluation tools, while article type has a big effect. These 2 measures differ significantly for 2 groups: (a) nonprimary research or evidence-based research are more highly cited but not highly recommended, while (b) translational research or transformative research are more highly recommended but have fewer citations. This can be expected, since citation activity is usually practiced by academic authors while the potential for scientific revolutions and the suitability for clinical practice of an article should be investigated from a practitioners' perspective. We conclude with a recommendation that the application of bibliometric approaches in research evaluation should consider the proportion of 3 types of publications: evidence-based research, transformative research, and translational research. The latter 2 types are more suitable for assessment through peer review.