Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions

arXiv:2204.13704v2 Announce Type: replace
Abstract: Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) that they typically map entities into Euclidean space and treat relations as transformations of entities. Recently, some Euclidean KGE methods have been enhanced to model semantic hierarchies commonly found in KGs, improving the performance of link prediction. To embed hierarchical data, hyperbolic space has emerged as a promising alternative to traditional Euclidean space, offering high fidelity and …

arxiv cs.ai cs.lg dimensions embeddings graph hierarchical knowledge knowledge graph link prediction low prediction type