著者

蛭子 琢磨 市瀬 龍太郎 Ebisu Takuma Ichise Ryutaro

雑誌

人工知能学会研究会資料

巻号頁・発行日

vol.44, no.3, pp.1-6, 2018-03-18

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Knowledge graphs are useful for many artificial intelligence tasks. However, knowledge graphs often have missing facts. To populate knowledge graphs, the graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidates triples. Translation-based models are part of knowledge graph embedding models and they employ the translation-based principle. The principle can efficiently capture the rules of a knowledge graph, however TransE, the original translation-based model, has some problems. To solve them many extensions of TransE have been proposed. In this paper, we discuss such problems and models.