论文标题
知识图完成的评论
A Review of Knowledge Graph Completion
论文作者
论文摘要
事实证明，信息提取方法可有效从结构化或非结构化数据中提取三重提取。以（头部实体，关系，尾部实体）形式组织这样的三元组的组织称为知识图的构建（kgs）。当前的大多数知识图都是不完整的。为了在下游任务中使用kg，希望预测千克中缺失的链接。最近，通过将实体和关系嵌入到低维矢量空间中，以根据先前访问的三元组来预测未知的三元组，从而对KGS表示不同的方法。根据如何独立或依赖对三元组进行处理，我们将知识图完成的任务分为传统和图形神经网络表示学习，并更详细地讨论它们。在传统的方法中，每个三重三倍将独立处理，并在基于GNN的方法中进行处理，三倍也考虑了他们的当地社区。查看全文
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. View Full-Text
