@inproceedings{das-etal-2020-probabilistic,
    title = "Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion",
    author = "Das, Rajarshi  and
      Godbole, Ameya  and
      Monath, Nicholas  and
      Zaheer, Manzil  and
      McCallum, Andrew",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.427/",
    doi = "10.18653/v1/2020.findings-emnlp.427",
    pages = "4752--4765",
    abstract = "A case-based reasoning (CBR) system solves a new problem by retrieving {\textquoteleft}cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an {\textquotedblleft}open-world{\textquotedblright} setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method."
}