A construction technology of automatic reasoning system based on knowledge graph

Guobin Wu; Jian Liang; Xi Jin; Xiao Wang; Zhewen Zhang

doi:10.1117/12.2645937

23 August 2022 A construction technology of automatic reasoning system based on knowledge graph

Guobin Wu, Jian Liang, Xi Jin, Xiao Wang, Zhewen Zhang

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123051A (2022) https://doi.org/10.1117/12.2645937
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

With the rapid development of artificial intelligence, the research on perceptual intelligence is becoming more and more mature. For the next stage of cognitive intelligence research, knowledge graph (KG) is one of the key directions. Knowledge reasoning is an important part of KG and has a wide range of application requirements. At present, knowledge reasoning methods in large-scale KG still have problems of poor interpretability, low reasoning accuracy and efficiency. Knowledge reasoning methods based on deep reinforcement learning have better interpretability and stronger reasoning ability. This paper introduces the research progress of knowledge reasoning based on KG, and makes an analysis of the current knowledge reasoning methods based on knowledge representation, relational path and deep reinforcement learning. At the same time, this paper conducts link prediction and fact prediction experiments on related knowledge reasoning methods on NELL-995 and FB15K-237 datasets, and summarizes the datasets, evaluation methods and indicators involved in these experiments. In conclusion, the proposed future knowledge reasoning model is based on the fusion knowledge representation, relational path with deep reinforcement learning methods.

Citation Download Citation

Guobin Wu, Jian Liang, Xi Jin, Xiao Wang, and Zhewen Zhang "A construction technology of automatic reasoning system based on knowledge graph", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123051A (23 August 2022); https://doi.org/10.1117/12.2645937

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KEYWORDS

Neural networks

Evolutionary algorithms

Performance modeling

Artificial intelligence

Machine learning

Reliability

Model-based design

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