@inproceedings{209c4f135d54489b980d997b5d2d7fda,
title = "A roadmap to domain knowledge integration in machine learning",
abstract = "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 resource. 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.",
keywords = "Constraint, Domain, Knowledge, Loss",
author = "Gupta, {Himel Das} and Sheng, {Victor S.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th IEEE International Conference on Knowledge Graph, ICKG 2020 ; Conference date: 09-08-2020 Through 11-08-2020",
year = "2020",
month = aug,
doi = "10.1109/ICBK50248.2020.00030",
language = "English",
series = "Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "145--151",
editor = "Enhong Chen and Grigoris Antoniou and Xindong Wu and Vipin Kumar",
booktitle = "Proceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020",
}