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 ...
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.