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A Roadmap to Domain Knowledge Integration in Machine Learning

arXiv 2212.05712 ↗First posted 12 Dec 2022cs.LG cs.CV cs.IRcited by 0

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Abstract (arXiv)

Many machine learning algorithms have been developed in recent years to enhance the performance of a model in different aspects of artificial intelligence. But the problem persists due to inadequate data and resources. Integrating knowledge in a machine learning model can help to overcome these obstacles up to a certain degree. Incorporating knowledge is a complex task though because of various forms of knowledge representation. In this paper, we will give a brief overview of these different forms of knowledge integration and their performance in certain machine learning tasks.

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References · 12

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
  4. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 323
  5. 2019ERNIE: Enhanced Representation through Knowledge Integrationcited by 301
  6. 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
  7. 2018Open Domain Question Answering Using Early Fusion of Knowledge Bases and Textcited by 167
  8. 2016Harnessing Deep Neural Networks with Logic Rulescited by 135
  9. 2016Label-Free Supervision of Neural Networks with Physics and Domain Knowledgecited by 74
  10. 2019Embedding Symbolic Knowledge into Deep Networkscited by 49
  11. 2018Deep Generative Models with Learnable Knowledge Constraintscited by 23
  12. 2018Innateness, AlphaZero, and Artificial Intelligencecited by 10

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