文章基本信息

标题：A Flexible Translation-Based Knowledge Graph Embedding Adapting Unobserved Entities
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作者：A-Yeong Kim ; Seong-Bae Park ; Sang-Jo Lee 等
期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2016
卷号：10
期号：11
页码：49-58
DOI：10.14257/ijseia.2016.10.11.05
出版社：SERSC
摘要：This paper proposes a flexible translation-based knowledge graph embedding that learns unobserved entities by moving positions of embedding vectors from existed embedding space. To reflect unobserved entities, previous methods tend to learn knowledge graphs all over again. This process causes high cost of calculation. Thus, this paper introduces an adjusting method which moves positions of learned embedding vectors according to unobserved entity. This idea is based on TransE model that is a one of translation-based methods. According to experiments, the proposed method shows the plausibility at link prediction task and triple classification task. These experimental results prove that reducing learning cost is a crucial issue for embedding knowledge graphs.
关键词：Knowledge Graph Embedding; Adaptive Learning; Translation-Based ; Embedding; Knowledge Graph Completion; Link Prediction; Triple Classification