论文标题
知识图和知识网络：故事简短
Knowledge Graphs and Knowledge Networks: The Story in Brief
论文作者
论文摘要
知识图（KGS）以结构化形式表示现实世界嘈杂的原始信息，从而捕获实体之间的关系。但是，对于动态现实世界的应用程序，例如社交网络，推荐系统，计算生物学，关系知识表示已成为一个充满挑战的研究问题，在这些问题中，需要随着时间的推移表示变化的节点，属性和边缘。在过去几年中，搜索引擎对用户查询的响应的演变部分是因为诸如Google KG之类的KG的作用。 KG从链接预测，实体关系预测，节点分类到建议和问答系统的各种AI应用程序都为各种AI应用做出了贡献。本文是为了总结KG为AI的旅程的尝试。
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.
