Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)
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Title
Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)
Authors
Keywords
Explainable Artificial Intelligence, Knowledge Graph, Demand forecasting, Smart manufacturing, Confidentiality, Privacy
Journal
Information Fusion
Volume 81, Issue -, Pages 91-102
Publisher
Elsevier BV
Online
2021-12-01
DOI
10.1016/j.inffus.2021.11.015
References
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- (2019) Elodie Thiéblin et al. Semantic Web
- Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
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- (2018) Fei Tao et al. JOURNAL OF MANUFACTURING SYSTEMS
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- A survey of current Link Discovery frameworks
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- (2014) Panče Panov et al. DATA MINING AND KNOWLEDGE DISCOVERY
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- (2014) D. Lengu et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Using adaptive network-based fuzzy inference system to forecast automobile sales
- (2011) Fu-Kwun Wang et al. EXPERT SYSTEMS WITH APPLICATIONS
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- (2011) A.A. Syntetos et al. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
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- (2010) Xiaomeng Chang et al. JOURNAL OF MECHANICAL DESIGN
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