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Abstract Graphs and Abstract Paths for Knowledge Graph Completion

机译:知识图完成的抽象图和抽象路径

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Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete. Information that we induce from such graphs - e.g. entity embeddings, relation representations or patterns - will be affected by the imbalance in the information captured in the graph - by biasing representations, or causing us to miss potential patterns. To partially compensate for this situation we describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the results of link prediction using this information.
机译:以机器友好的格式提供大量事实的知识图是不完整的。我们从此类图表中得出的信息-例如实体嵌入,关系表示或模式-会受到图形中捕获的信息不平衡的影响-会偏向表示,或导致我们错过潜在的模式。为了部分补偿这种情况,我们描述了一种表示知识图的方法,该知识图捕获了原始扩展信息的内涵表示。这种表示非常紧凑,并且它抽象出各个链接,从而使我们能够找到更好的候选路径,如使用此信息的链接预测结果所示。

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