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.

著者

Munasinghe Lankeshwara Ichise Ryutaro

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.26, 2012

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Study of link evolution in social networks has become a vital research topic. In this paper, we will present the analytical details and results of the experiments we conducted to study the link evolution and information flow in social networks.