Related papers: Integration of knowledge and data in machine learning

Integration of knowledge and data in machine learning

URL: http://arxiv.org/abs/2202.10337v1
Date: Tue, 15 Feb 2022 10:35:53 GMT
Title: Integration of knowledge and data in machine learning
Authors: Yuntian Chen, Dongxiao Zhang
Abstract summary: Through knowledge embedding, barriers between knowledge and data can be broken, and machine learning models with physical common sense can be formed. Knowledge discovery takes advantage of machine learning to extract new knowledge from observations. This study not only summarizes and analyzes the existing literature, but also proposes research gaps and future opportunities.
Score: 0.456877715768796
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Scientific research's duty and goal is to comprehend and explore the world, as well as to modify it based on experience and knowledge. Knowledge embedding and knowledge discovery are two significant methods of integrating knowledge and data. Through knowledge embedding, the barriers between knowledge and data can be broken, and machine learning models with physical common sense can be formed. Meanwhile, humans' understanding of the world is always limited, and knowledge discovery takes advantage of machine learning to extract new knowledge from observations. Not only may knowledge discovery help researchers better grasp the nature of physics, but it can also help them conduct knowledge embedding research. A closed loop of knowledge generation and usage are formed by combining knowledge embedding with knowledge discovery, which can improve the robustness and accuracy of the model and uncover unknown scientific principles. This study not only summarizes and analyzes the existing literature, but also proposes research gaps and future opportunities.

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