A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling DOI Creative Commons

Yuhao Liu,

Jia-Yuan Han,

Peng Yan

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(10), P. 723 - 723

Published: Oct. 12, 2024

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool analyzing manufacturing resources. On this basis, study proposes novel KG construction method IMaaS. First, E-R diagram is used to divide constant variable entities set attributes relationships. Then, triplets are named, value space set, schema layer constructed. Finally, related information about fill data layer, then, knowledge generated. Meanwhile, utilizes desktop FDM 3D printing case example validation. The proposed in can enhance accuracy maintainability equipment management sector, effectively promoting subsequent activities such management, analysis, decision-making.

Language: Английский

Knowledge Graph Construction: Extraction, Learning, and Evaluation DOI Creative Commons

S. -K. Choi,

Yuchul Jung

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3727 - 3727

Published: March 28, 2025

A Knowledge Graph (KG), which structurally represents entities (nodes) and relationships (edges), offers a powerful flexible approach to knowledge representation in the field of Artificial Intelligence (AI). KGs have been increasingly applied various domains—such as natural language processing (NLP), recommendation systems, search, medical diagnostics—spurring continuous research on effective methods for their construction maintenance. Recently, efforts combine large models (LLMs), particularly those aimed at managing hallucination symptoms, with gained attention. Consequently, new approaches emerged each phase KG development, including Extraction, Learning Paradigm, Evaluation Methodology. In this paper, we focus major publications released after 2022 systematically examine process along three core dimensions: Specifically, investigate (1) large-scale data preprocessing multimodal extraction techniques Extraction domain, (2) refinement traditional embedding application cutting-edge techniques—such Neural Networks, Transformers, LLMs—in (3) both intrinsic extrinsic metrics well ensure interpretability reliability.

Language: Английский

Citations

0
A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturing DOI
K.B. Mustapha

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103066 - 103066

Published: Dec. 27, 2024

Language: Английский

Citations

3
A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling DOI Creative Commons

Yuhao Liu,

Jia-Yuan Han,

Peng Yan

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(10), P. 723 - 723

Published: Oct. 12, 2024

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool analyzing manufacturing resources. On this basis, study proposes novel KG construction method IMaaS. First, E-R diagram is used to divide constant variable entities set attributes relationships. Then, triplets are named, value space set, schema layer constructed. Finally, related information about fill data layer, then, knowledge generated. Meanwhile, utilizes desktop FDM 3D printing case example validation. The proposed in can enhance accuracy maintainability equipment management sector, effectively promoting subsequent activities such management, analysis, decision-making.

Language: Английский

Citations

0