A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Abstract
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.
Cite
@article{arxiv.2210.00105,
title = {A Decade of Knowledge Graphs in Natural Language Processing: A Survey},
author = {Phillip Schneider and Tim Schopf and Juraj Vladika and Mikhail Galkin and Elena Simperl and Florian Matthes},
journal= {arXiv preprint arXiv:2210.00105},
year = {2022}
}
Comments
Accepted to AACL-IJCNLP 2022