# Knowledge Modelling and Active Learning in Manufacturing

arXiv: https://arxiv.org/abs/2107.02298 · HTML: https://mlsanity.com/paper/2107.02298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 118 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [Generative Adversarial Active Learning](https://mlsanity.com/paper/1702.07956/) (2017)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (2020)
- [Bayesian Generative Active Deep Learning](https://mlsanity.com/paper/1904.11643/) (2019)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [Adversarial Sampling for Active Learning](https://mlsanity.com/paper/1808.06671/) (2018)
- [A Survey of Active Learning for Text Classification using Deep Neural Networks](https://mlsanity.com/paper/2008.07267/) (2020)
- [Open Question Answering with Weakly Supervised Embedding Models](https://mlsanity.com/paper/1404.4326/) (2014)
- [Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage](https://mlsanity.com/paper/2004.10931/) (2020)
- [Active WeaSuL: Improving Weak Supervision with Active Learning](https://mlsanity.com/paper/2104.14847/) (2021)
- [Actionable Cognitive Twins for Decision Making in Manufacturing](https://mlsanity.com/paper/2103.12854/) (2021)
- [XAI-KG: knowledge graph to support XAI and decision-making in manufacturing](https://mlsanity.com/paper/2105.01929/) (2021)
- [Towards Active Learning Based Smart Assistant for Manufacturing](https://mlsanity.com/paper/2103.16177/) (2021)
- [Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.04556/) (2020)

## 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.