Before revision: We improve both the open-set generalization and efficiency of link prediction on knowledge graphs by leveraging the contexts of entities and relations in a novel semantic triple encoder. Most previous methods , e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models.
After revision: Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them. Prevalent graph embedding approaches , e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models.