Knowledge Graph and Deep Learning Empowering Content Security

Abstract

Abstract: The early idea of knowledge graph originated from the Semantic Web proposed by Tim Berners Lee, the Father of World Wide Web. It aims to use the graph structure to model the relationship and knowledge between the world. Deep learning is derived from the study of artificial neural networks. It uses deep neural networks to learn knowledge from massive data. Its advantage is that it can automatically learn feature from massive data instead of manual feature engineering. Integrating the method of deep learning into the application of knowledge map is one of the current research hot spots. In the fields of automated knowledge acquisition, knowledge representation learning and reasoning, and largescale graph mining and analysis, deep learning and knowledge graph have made a lot of progress. For the content security field, the knowledge graph can effectively improve the retrieval efficiency of content security, enhance the understanding and interpretability of text content, and help content security to move toward the era of knowledge intelligence.

Key words: knowledge graph, deep learning, content security, representation learning, graph neural network

摘要： 知识图谱的早期理念来自于Web之父Tim Berners Lee于1998年提出的Semantic Web，旨在利用图结构建模世界万物之间的关联关系和知识. 深度学习源于人工神经网络的研究，其利用深层网络从海量数据学习知识，其优点是可以无需手工获取特征. 将深度学习的方法融入知识图谱的应用中，是当下的研究热点之一. 在自动化知识获取、知识表示学习与推理、大规模图挖掘与分析等领域，深度学习和知识图谱结合都获得了不少研究成果. 对于内容安全领域来说，知识图谱可以有效提升内容安全的检索效率，提升对文本内容的理解和可解释性，助力内容安全走向知识智能时代.

关键词: 知识图谱, 深度学习, 内容安全, 表示学习, 图神经网络

陈华钧耿玉霞叶志权邓淑敏. “知识图谱+深度学习”赋能内容安全[J]. 信息安全研究, 2019, 5(11): 975-980.

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