Abstract
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.- Anthology ID:
- 2020.textgraphs-1.9
- Volume:
- Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Dmitry Ustalov, Swapna Somasundaran, Alexander Panchenko, Fragkiskos D. Malliaros, Ioana Hulpuș, Peter Jansen, Abhik Jana
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 79–84
- Language:
- URL:
- https://aclanthology.org/2020.textgraphs-1.9
- DOI:
- 10.18653/v1/2020.textgraphs-1.9
- Cite (ACL):
- Haseeb Shah, Johannes Villmow, and Adrian Ulges. 2020. Relation Specific Transformations for Open World Knowledge Graph Completion. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 79–84, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Relation Specific Transformations for Open World Knowledge Graph Completion (Shah et al., TextGraphs 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.textgraphs-1.9.pdf
- Code
- haseebs/rst-owe
- Data
- FB15k, FB15k-237