Haseeb Shah


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Relation Specific Transformations for Open World Knowledge Graph Completion
Haseeb Shah | Johannes Villmow | Adrian Ulges
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

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 to graph-based embedding space, where the closed-world 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.