@inproceedings{shah-etal-2020-relation,
title = "Relation Specific Transformations for Open World Knowledge Graph Completion",
author = "Shah, Haseeb and
Villmow, Johannes and
Ulges, Adrian",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Hulpu{\textcommabelow{s}}, Ioana and
Jansen, Peter and
Jana, Abhik",
booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.textgraphs-1.9",
doi = "10.18653/v1/2020.textgraphs-1.9",
pages = "79--84",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Relation Specific Transformations for Open World Knowledge Graph Completion
%A Shah, Haseeb
%A Villmow, Johannes
%A Ulges, Adrian
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Hulpu\textcommabelows, Ioana
%Y Jansen, Peter
%Y Jana, Abhik
%S Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F shah-etal-2020-relation
%X 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.
%R 10.18653/v1/2020.textgraphs-1.9
%U https://aclanthology.org/2020.textgraphs-1.9
%U https://doi.org/10.18653/v1/2020.textgraphs-1.9
%P 79-84
Markdown (Informal)
[Relation Specific Transformations for Open World Knowledge Graph Completion](https://aclanthology.org/2020.textgraphs-1.9) (Shah et al., TextGraphs 2020)
ACL