Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content

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• 作者：Giannis Chantas (16)
  Alexandros Kitsikidis (16)
  Spiros Nikolopoulos (16)
  Kosmas Dimitropoulos (16)
  Stella Douka (17)
  Ioannis Kompatsiaris (16)
  Nikos Grammalidis (16)
  
  16. Centre for Research and Technology Hellas ; Information Technologies Institute ; 6th km Xarilaou-Thermi ; Thessaloniki ; Greece
  17. Department of Physical Education and Sport Science ; Aristotle University of Thessaloniki ; Thessaloniki ; Greece
  
• 关键词：Semantic analysis ; Intangible cultural heritage ; Multi ; entity bayesian networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8926
• 期：1
• 页码：355-369
• 全文大小：834 KB
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• 作者单位：Computer Vision - ECCV 2014 Workshops
• 丛书名：978-3-319-16180-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper we introduce Multi-Entity Bayesian Networks (MEBNs) as the means to combine first-order logic with probabilistic inference and facilitate the semantic analysis of Intangible Cultural Heritage (ICH) content. First, we mention the need to capture and maintain ICH manifestations for the safeguarding of cultural treasures. Second, we present the MEBN models and stress their key features that can be used as a powerful tool for the aforementioned cause. Third, we present the methodology followed to build a MEBN model for the analysis of a traditional dance. Finally, we compare the efficiency of our MEBN model with that of a simple Bayesian network and demonstrate its superiority in cases that demand for situation-specific treatment.