research-article

The Comprehensibility Assessment of Visualization of Semantic Data Representation (VSDR) Reflecting User Capability of Knowledge Exploration and Discovery

Authors:

Natanun Kanjanakuha,

Paul Janecek,

Churee TechawutAuthors Info & Claims

ICCCM '19: Proceedings of the 7th International Conference on Computer and Communications Management

Pages 195 - 199

https://doi.org/10.1145/3348445.3348450

Published: 27 July 2019 Publication History

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Abstract

The visual representations of search result querying from encyclopedia by semantic searching tools are rarely found on the internet. Most of the available tools require SPARQL to make some specific queries which are difficult to use, and the visual representation of results on a screen is too complicated to comprehend. The necessity of reducing the complexity of visual structure, supporting user interaction, and increasing the perception of knowledge discovery is considerable. The framework of Visualization of Semantic Data Representation (VSDR) is designed by using the 3-layer approach based on the hyperbolic tree model for visual representation of results from the encyclopedia. The architecture of the searching tool is also designed for novice users. This study presents the result of VSDR usage to the extent of the VSDR's capabilities in terms of comprehensibility, and how the user can perceive a VSDR structure with discoverable knowledge. The result shows that VSDR is an effective searching tool for knowledge exploration and discovery. It has a recognition support reflected in users' perception. It also helped users find answers, learn and gain new knowledge through the representation.

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Cited By

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Chen XLiu B(2023)Research Progress of Tumor Big Data VisualizationElectronics10.3390/electronics1203074312:3(743)Online publication date: 2-Feb-2023
https://doi.org/10.3390/electronics12030743
Khan ARashid UAhmed N(2022)An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web ContentIEEE Access10.1109/ACCESS.2022.317759710(56316-56330)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177597

Index Terms

The Comprehensibility Assessment of Visualization of Semantic Data Representation (VSDR) Reflecting User Capability of Knowledge Exploration and Discovery
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
  2. Visualization
    1. Visualization techniques
      1. Hyperbolic trees

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Published In

ICCCM '19: Proceedings of the 7th International Conference on Computer and Communications Management

July 2019

260 pages

ISBN:9781450371957

DOI:10.1145/3348445

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Chongqing University of Posts and Telecommunications

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2019

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ICCCM 2019: The 7th International Conference on Computer and Communications Management

July 27 - 29, 2019

Bangkok, Thailand

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Cited By

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Chen XLiu B(2023)Research Progress of Tumor Big Data VisualizationElectronics10.3390/electronics1203074312:3(743)Online publication date: 2-Feb-2023
https://doi.org/10.3390/electronics12030743
Khan ARashid UAhmed N(2022)An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web ContentIEEE Access10.1109/ACCESS.2022.317759710(56316-56330)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177597

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From Search to Discovery with Visual Exploration Tools

Knowledge Discovery and Data Visualization: Theories and Perspectives

Surveying the complementary role of automatic data analysis and visualization in knowledge discovery