@inproceedings{markowitz-etal-2022-statik,
    title = "{S}t{ATIK}: Structure and Text for Inductive Knowledge Graph Completion",
    author = "Markowitz, Elan  and
      Balasubramanian, Keshav  and
      Mirtaheri, Mehrnoosh  and
      Annavaram, Murali  and
      Galstyan, Aram  and
      Ver Steeg, Greg",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.findings-naacl.46/",
    doi = "10.18653/v1/2022.findings-naacl.46",
    pages = "604--615",
    abstract = "Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing links. In this paper, we present \textbf{StATIK}{--}\textbf{St}ructure \textbf{A}nd \textbf{T}ext for \textbf{I}nductive \textbf{K}nowledge Completion. StATIK uses Language Models to extract the semantic information from text descriptions, while using Message Passing Neural Networks to capture the structural information. StATIK achieves state of the art results on three challenging inductive baselines. We further analyze our hybrid model through detailed ablation studies."
}