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Knowledge Modelling and Active Learning in Manufacturing

arXiv 2107.02298 ↗First posted 5 Jul 2021cs.AIcited by 0

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Abstract (arXiv)

The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information. Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and configurations. Both can be used to generate new knowledge through deductive inference and identify missing knowledge. While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. Active learning can be used to identify the most informative data instances for which to obtain users' feedback, reduce friction, and maximize knowledge acquisition. By combining semantic technologies and active learning, multiple use cases in the manufacturing domain can be addressed taking advantage of the available knowledge and data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 118 references that are in the map. First posted on arXiv 5 Jul 2021.

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19 of 118 references.

References · 19

  1. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  2. 2020Knowledge Graphscited by 619
  3. 2019Variational Adversarial Active Learningcited by 284
  4. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 150
  5. 2018Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Networkcited by 74
  6. 2017Generative Adversarial Active Learningcited by 69
  7. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  8. 2020Learning from Rules Generalizing Labeled Exemplarscited by 59
  9. 2019Bayesian Generative Active Deep Learningcited by 57
  10. 2020Interactive Weak Supervision: Learning Useful Heuristics for Data Labelingcited by 52
  11. 2018Adversarial Sampling for Active Learningcited by 51
  12. 2020A Survey of Active Learning for Text Classification using Deep Neural Networkscited by 50
7 more
  1. 2014Open Question Answering with Weakly Supervised Embedding Modelscited by 26
  2. 2020Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselagecited by 14
  3. 2021Active WeaSuL: Improving Weak Supervision with Active Learningcited by 12
  4. 2021Actionable Cognitive Twins for Decision Making in Manufacturingcited by 5
  5. 2021XAI-KG: knowledge graph to support XAI and decision-making in manufacturingcited by 5
  6. 2021Towards Active Learning Based Smart Assistant for Manufacturingcited by 3
  7. 2020Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddingscited by 2

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