Domain Representation for Knowledge Graph Embedding
This work addresses the limitation of ignoring hierarchical knowledge in knowledge graph embeddings, which is important for applications like link prediction, but it appears incremental as it builds on existing embedding models.
The paper tackles the problem of representing hierarchical knowledge in knowledge graph embeddings by introducing domain representations to group entities with similar attributes, resulting in significant improvements over state-of-the-art baseline models in link prediction.
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.