Difference between revisions of "Data-based Heritage Interpretation - An Ontology Design for Interpretive Information of Korean Cultural Heritages"

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Revision as of 00:52, 11 January 2018

Thesis for the Degree of Master of Arts in Cultural Informatics


by Lyndsey Dianna TWINING


The Graduate School of Korean Studies

The Academy of Korean Studies

Seongnam, Korea


Defense Committee

Chairperson: Milan Hejtmanek

Member: Jeongsoo Shin

Advisor: Hyeon Kim


PDF Download


Table of Contents

  1. Introduction
    1. Background and Objectives
    2. Methodology
  2. What is Heritage Interpretation?
    1. Understanding Heritage Interpretation
      1. What is Heritage?
      2. What is Heritage Interpretation?
    2. The Ideals of Heritage Interpretation
      1. Clear / Accurate
      2. Personalized / Tailored
      3. Contextualized / Holistic
      4. Facilitates Engagement
      5. Sustainable / Innovative
    3. Definitions of Other Key Terms
  3. Current Status of Korean Cultural Heritage Interpretation
    1. Korean Cultural Heritages and Managing Institutions
    2. Resources
      1. Analog
      2. Digital – Offline
      3. Digital – Online
      4. Cultural Heritage Administration Metadata
      5. The Bridge Between Analog and Online
    3. Processes
      1. On-Site Interpretive Text Guidelines
    4. Content
  4. Evaluation of and Suggestions for Korean Cultural Heritage Interpretation
    1. Clear / Accurate
    2. Personal / Tailored
    3. Contextualized / Holistic
    4. Facilitates Engagement
    5. Sustainable / Innovative
  5. A Data-based Perspective on Heritage Interpretation
    1. Digital Perspectives on Heritage Interpretation
    2. The Unique Capabilities of the Database
    3. What is a Graph Database?
    4. What is an Ontology?
    5. The Ideals of Heritage Interpretation from a Data-based Perspective
  6. Ontology Design
    1. Existing Heritage Ontologies
    2. Ontology Scope
    3. Design Strategy
    4. Design
      1. Node Labels
      2. Node Properties
      3. Relationship Labels
      4. Relationship Properties
      5. Relationships
  7. Examples of Data-based Heritage Interpretation
    1. Improving Accuracy and Clarity
    2. Making Interpretation Personalized
    3. Conveying Not Just the Heritage, But Its Context, Too
    4. Facilitating Further Engagement
    5. Ensuring Long-term Sustainability and Innovation
  8. Conclusion
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