@inproceedings{saeedizade-etal-2022-kgrefiner,
    title = "{KGR}efiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods",
    author = "Saeedizade, Mohammad Javad  and
      Torabian, Najmeh  and
      Minaei-Bidgoli, Behrouz",
    editor = {Fan, Angela  and
      Gurevych, Iryna  and
      Hou, Yufang  and
      Kozareva, Zornitsa  and
      Luccioni, Sasha  and
      Sadat Moosavi, Nafise  and
      Ravi, Sujith  and
      Kim, Gyuwan  and
      Schwartz, Roy  and
      R{\"u}ckl{\'e}, Andreas},
    booktitle = "Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.sustainlp-1.3",
    doi = "10.18653/v1/2022.sustainlp-1.3",
    pages = "10--16",
    abstract = "Link Prediction is the task of predicting missing relations between knowledge graph entities (KG). Recent work in link prediction mainly attempted to adapt a model to increase link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources. This paper proposes the refinement of knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models have significantly less complexity than deep learning approaches; this motivated us to improve their accuracy. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, and Mean Reciprocal Rank.",
}