Path Ranking Model for Entity Prediction.

ICME(2021)

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摘要
Knowledge graphs (KGs) often encounter knowledge incompleteness, necessitating a demand for KG completion. Path-based methods are one of the most important approaches to this task. However, since the number of entities is much larger than that of relations in a knowledge graph, existing path-based methods are only used to predict the relations between entity pairs, and are rarely applied to solve the entity prediction task. To address the issue, this paper proposes a new framework called Path Ranking Model (PRM) for the knowledge graph completion task. Our key idea is to exploit both the observable patterns and latent semantic information in relation paths to predict the entities. Extensive experiments on public popular datasets demonstrate the effectiveness of our proposed framework in the entity prediction task.
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关键词
Knowledge graph,KGC,path ranking
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