Automated Construction Method of Knowledge Graph Driven by Deep Learning

Liangtao Yang

doi:10.62051/ijcsit.v7n1.01

Authors

Liangtao Yang

DOI:

https://doi.org/10.62051/ijcsit.v7n1.01

Keywords:

Knowledge graph, Natural language processing, Deep learning, Knowledge representation

Abstract

Against the backdrop of the rapid development of big data and artificial intelligence, knowledge graphs are increasingly becoming an important tool for information organization and knowledge discovery. This paper systematically reviews the basic construction process of knowledge graphs and the core technologies of deep learning, with a focus on analyzing the applications of convolutional neural networks, graph neural networks, and sequence models in the automated construction of knowledge graphs. This paper focuses on deep learning-driven automated knowledge graph construction methods, exploring how to leverage deep learning techniques to enhance the efficiency and accuracy of knowledge extraction, fusion, and reasoning. This paper argues that the combination of deep learning and knowledge graphs not only broadens the application boundaries of artificial intelligence but also provides more solid knowledge support for intelligent search, recommendation systems, and semantic understanding.

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