Relation Specific Transformations for Open World Knowledge Graph Completion
Haseeb Shah, Johannes Villmow, Adrian Ulges · 2020
We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.Our model learns relation-specific transformation functions from text-based embedding space to graph-based embedding space, where the closedworld link prediction model can be applied.We demonstrate state-of-the-art results on common open-world benchmarks and show that our approach benefits from relation-specific transformation functions (RST), giving substantial improvements over a relation-agnostic approach.