面向人工智能深度学习的知识图谱补全技术与应用综述

摘要:

知识图谱旨在为各种领域提供更加全面可靠的服务，在实际应用中的价值不可估量，为了使其不断更新和趋于完整，知识图谱补全技术开始被提出；近几年，随着人工智能和深度学习的兴起，许多国内外学者对知识图谱补全方向进行深入研究，出现了很多面向人工智能深度学习的知识图谱补全模型，但相关的文献综述却并不多；为了提供一个全面了解该领域的框架，有助于读者能够掌握当前的研究进展和应用情况，并为未来的研究和应用提供一些参考；通过介绍其概念和典型的知识图谱，从深度学习的知识补全技术的三个角度展开，分析和归纳了目前基于深度学习的知识图谱补全模型，探讨了不同模型的优缺点及改进模型；同时，讨论了现阶段知识图谱补全任务所存在的问题和挑战，并探索了该领域的应用方向和发展前景；综上所述，深度学习在知识图谱补全中具有巨大的发掘价值，亟待学者们进行更深入的研究和进一步地创新。

Abstract:

Knowledge graph aims to provide more comprehensive and reliable services for various fields, the value in practical applications is immeasurable, in order to make it constantly updated and complete, knowledge graph completion technology began to be proposed; In recent years, with the rise of artificial intelligence and deep learning, many scholars at home and abroad have conducted in-depth research on the direction of knowledge graph completion, and many knowledge graph completion models for artificial intelligence deep learning have emerged, but there are not many relevant literature reviews. In order to provide a comprehensive understanding of the field, it helps readers to grasp the current research progress and applications, and provides some references for future research and applications; By introducing its concept and typical knowledge graph, the current knowledge graph completion model based on deep learning is analyzed and summarized from the three perspectives of deep learning knowledge completion technology, and the advantages and disadvantages of different models and the improved models are discussed. At the same time, the problems and challenges of the knowledge graph completion task at this stage are discussed, and the application direction and development prospects of this field are explored. In summary, deep learning has great exploration value in knowledge graph completion, and scholars are urgently needed to conduct more in-depth research and further innovation.