<b>A model for knowledge visualization based on visual archetypes</b> - doi: 10.4025/actascitechnol.v34i4.10435

  • Héctor Andrés Melgar Sasieta Pontificia Universidad Católica del Perú, Departamento de Ingeniería, Sección de Ingeniería Informática
  • Fabiano Duarte Beppler Instituto Stela
  • Roberto Carlos do Santos Pacheco Universidade Federal de Santa Catarina
Keywords: knowledge visualization, knowledge retrieval, semantic annotation, ontology

Abstract

This paper presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using visual archetypes. Archetypes are structures that contain visual representations of the real world that are known a priori by the target group, and which have semantic structures for identifying the entities of the domain represented in each region. The proposed model is supported by the framework for knowledge visualization proposed by Burkhard and describes the users’ interactions with visual archetypes. The user through the archetypes can retrieve and view the knowledge related to the entities represented in the archetypes’ images. A prototype was developed to demonstrate the feasibility of the model using archetypes in the biomedical field, the Foundational Model of Anatomy and the Unified Medical Language System as domain knowledge and the Scientific Electronic Library Online database as a document repository. The use of visual representations in archetypes facilitates the dissemination of knowledge, because these are part of the world view of users and can easily be related with prior knowledge. Visual representations are processed quickly in the brain and require less effort than the processing of textual information.

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Published
2012-05-31
How to Cite
Sasieta, H. A. M., Beppler, F. D., & Pacheco, R. C. do S. (2012). <b>A model for knowledge visualization based on visual archetypes</b&gt; - doi: 10.4025/actascitechnol.v34i4.10435. Acta Scientiarum. Technology, 34(4), 381-389. https://doi.org/10.4025/actascitechnol.v34i4.10435
Section
Computer Science

 

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0.8
2019CiteScore
 
 
36th percentile
Powered by  Scopus