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(AI)和大数据的爆炸式增长，适当地组织和表示海量知识已经变得至关重要。作为图数据，知识图谱积累和传达现实世界的知识。人们已经充分认识到知识图谱有效地表示复杂信息;因此，最近几年，它们迅速受到学术界和工业界的关注。因此，为了深入了解知识图谱，本文给出了该领域的系统综述。具体而言，我们着重介绍了知识图谱的机遇和挑战。我们首先从两个方面评述了知识图谱的机遇: (1) 基于知识图谱构建AI系统; (2)知识图谱的潜在应用领域。然后，我们深入讨论了该领域中的一些重要技术挑战，如知识图嵌入，知识获取，知识图完成，知识融合和知识推理。我们期望本篇综述能为未来的研究和知识图谱的发展带来新的启示。