Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing | ScienceToStartup

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Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

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References (18)

[1]

Systematic review of predictive maintenance practices in the manufacturing sector

2025Abdeldjalil Benhanifia, Zied Ben Cheikh et al.

[2]

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

2024Darren Edge, Ha Trinh et al.

[3]

MLSea: A Semantic Layer for Discoverable Machine Learning

2024Ioannis Dasoulas, Duo Yang et al.

[4]

Combining Knowledge Graphs and Large Language Models to Ease Knowledge Access in Software Architecture Research

2024Angelika Kaplan, Jan Keim et al.

[5]

LLM4QA: Leveraging Large Language Model for Efficient Knowledge Graph Reasoning with SPARQL Query

2024Mingjing Lan, Yi Xia et al.

[6]

Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs

2023O. Ovadia, Meni Brief et al.

[7]

Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA

2023Dhruv Agarwal, Rajarshi Das et al.

[8]

Unifying Large Language Models and Knowledge Graphs: A Roadmap

2023Shirui Pan, Linhao Luo et al.

[9]

SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

2022Md. Rashad Al Hasan Rony, Uttam Kumar et al.

[10]

Querying knowledge graphs in natural language

2020Shi-Yu Liang, Kurt Stockinger et al.

[11]

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

2020Q. Liao, D. Gruen et al.

[12]

Ontologies for Industry 4.0

2019Veera Ragavan Sampath Kumar, Alaa Khamis et al.

[13]

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

2019Alejandro Barredo Arrieta, Natalia Díaz Rodríguez et al.

[14]

Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2019Laura von Rueden, S. Mayer et al.

[15]

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2018Amina Adadi, M. Berrada

[16]

Ontology-Based Data Access: A Survey

2018Guohui Xiao, Diego Calvanese et al.

[17]

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

2018G. Publio, Diego Esteves et al.

[18]

Towards A Rigorous Science of Interpretable Machine Learning

2017F. Doshi-Velez, Been Kim

Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

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Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

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