@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://aclanthology.org/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."
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<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 StATIK–Structure And Text for Inductive Knowledge 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.</abstract>
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%0 Conference Proceedings
%T StATIK: Structure and Text for Inductive Knowledge Graph Completion
%A Markowitz, Elan
%A Balasubramanian, Keshav
%A Mirtaheri, Mehrnoosh
%A Annavaram, Murali
%A Galstyan, Aram
%A Ver Steeg, Greg
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F markowitz-etal-2022-statik
%X 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 StATIK–Structure And Text for Inductive Knowledge 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.
%R 10.18653/v1/2022.findings-naacl.46
%U https://aclanthology.org/2022.findings-naacl.46/
%U https://doi.org/10.18653/v1/2022.findings-naacl.46
%P 604-615
Markdown (Informal)
[StATIK: Structure and Text for Inductive Knowledge Graph Completion](https://aclanthology.org/2022.findings-naacl.46/) (Markowitz et al., Findings 2022)
ACL
- Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, and Greg Ver Steeg. 2022. StATIK: Structure and Text for Inductive Knowledge Graph Completion. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 604–615, Seattle, United States. Association for Computational Linguistics.