With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.

翻译：随着人工智能和大数据的爆炸式增长，如何恰当地组织和表征海量知识变得至关重要。作为图数据，知识图谱积累并传递现实世界的知识。人们普遍认识到，知识图谱能够有效表征复杂信息，因此近年来迅速引起了学术界和工业界的关注。为了更深入地理解知识图谱，本文对该领域进行了系统性综述。具体而言，我们聚焦于知识图谱的机遇与挑战。首先从两个维度回顾知识图谱的机遇：(1) 基于知识图谱构建的人工智能系统；(2) 知识图谱的潜在应用领域。随后深入探讨该领域面临的关键技术挑战，包括知识图谱嵌入、知识获取、知识图谱补全、知识融合与知识推理。我们期望本综述能为知识图谱的未来研究与开发提供新启示。