Chun-Chih Wang
ABSTRACT
Knowledge graph completion is a critical issue because many applications benefit from their structural and rich resources. In this paper, we propose a method named TransN, which consid- ers the dependencies between triples and incorporates neighbor information dynamically. In experiments, we evaluate our model by link prediction and also conduct several qualitative analyses to prove effectiveness. Experimental results show that our model could integrate neighbor information effectively and outperform state-of-the-art models.
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Index Terms
- Translating Representations of Knowledge Graphs with Neighbors
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