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Knowledge Graph Embedding with Hierarchical Relation Structure
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
The rapid development of knowledge graphs (KGs), such as Freebase and WordNet, has changed the paradigm for AI-related applications. However, even though these KGs are impressively large, most of them are suffering from incompleteness, which leads to performance degradation of AI applications. Most existing researches are focusing on knowledge graph embedding (KGE) models. Nevertheless, those models simply embed entities and relations into latent vectors without leveraging the rich information
doi:10.18653/v1/d18-1358
dblp:conf/emnlp/ZhangZQLH18
fatcat:u7m2hkpi5na7fcpwwmonlln5ru