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Application of Artificial Intelligence Methods in Knowledge Graphs
- 1 Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing, China
* Author to whom correspondence should be addressed.
Abstract
This paper mainly explores the application of artificial intelligence (AI) technologies in knowledge graphs (KGs), focusing on how natural language processing (NLP), machine learning, and deep learning methods can achieve the automated construction of KGs. First, the paper introduces the basic concepts of KGs and the limitations of traditional construction methods. Then, it analyzes recent technological advancements in knowledge graph construction, data fusion, and reasoning, with particular emphasis on the application of graph convolutional neural networks (GCNs) in handling multi-relational data. Finally, the practical applications of KGs in business analytics, healthcare information systems, and recommendation systems are discussed, demonstrating their broad potential in data management and reasoning.
Keywords
Knowledge graph, Graph convolutional neural networks, Artificial intelligence.
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Cite this article
Yu,S. (2024). Application of Artificial Intelligence Methods in Knowledge Graphs. Applied and Computational Engineering,106,52-58.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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