TransET: Knowledge Graph Embedding with Entity Types

被引:11
|
作者
Wang, Peng [1 ,2 ]
Zhou, Jing [3 ]
Liu, Yuzhang [1 ]
Zhou, Xingchen [1 ]
机构
[1] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China
[2] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 211189, Peoples R China
[3] Nanjing Fiberhome Starrysky Co Ltd, Nanjing 211100, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
knowledge graph embedding; entity type; circular convolution;
D O I
10.3390/electronics10121407
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.
引用
收藏
页数:11
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