@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
      Hulpuș, 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://preview.aclanthology.org/fix-sig-urls/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."
}