Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques-Reference-Cited by-同舟云学术

Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

Published:2023 Issue: Volume:11 Page:127974-127986
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Razouk Houssam¹^ORCID,Liu Xing Lan²,Kern Roman³^ORCID

Affiliation:

1. Infineon Technologies Austria AG, Villach, Austria

2. Area of Knowledge Discovery, Know-Center GmbH, Graz, Austria

3. Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria

Funder

Electronic Components and Systems for European Leadership (ECSEL) AI4DI “Artificial Intelligence for Digitising Industry” project

ECSEL Joint Undertaking

European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Italy, Latvia, Belgium, Lithuania, France, Greece, Finland, Norway

“Edge AI Technologies for Optimised Performance Embedded Processing” project

Key Digital Technologies (KDT) JU

European Union’s Horizon Europe research and innovation program and Austria, Belgium, France, Greece, Italy, Latvia, Luxembourg, Netherlands, Norwayg

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10313282.pdf?arnumber=10313282

Reference38 articles.

1. Effective use of BERT in graph embeddings for sparse knowledge graph completion

2. Improving FMEA consistency in semiconductor manufacturing through text classification;tosone,2022

3. Commonsense Knowledge Base Completion with Structural and Semantic Context

4. Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

5. Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience

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