A roadmap to domain knowledge integration in machine learning

Himel Das Gupta, Victor S. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020
EditorsEnhong Chen, Grigoris Antoniou, Xindong Wu, Vipin Kumar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-151
Number of pages7
ISBN (Electronic)9781728181561
DOIs
StatePublished - Aug 2020
Event11th IEEE International Conference on Knowledge Graph, ICKG 2020 - Virtual, Nanjing, China
Duration: Aug 9 2020Aug 11 2020

Publication series

NameProceedings - 11th IEEE International Conference on Knowledge Graph, ICKG 2020

Conference

Conference11th IEEE International Conference on Knowledge Graph, ICKG 2020
Country/TerritoryChina
CityVirtual, Nanjing
Period08/9/2008/11/20

Keywords

  • Constraint
  • Domain
  • Knowledge
  • Loss

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