A Decade of Knowledge Graphs in Natural Language Processing: A Survey

Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 601–614 November 20–23, 2022. ©2022 Association for Computational Linguistics 601 A Decade of Knowledge Graphs in Natural Language Processing: A Survey Phillip Schneider1, Tim Schopf1, Juraj Vladika1, Mikhail Galkin2, Elena Simperl3 and Florian Matthes1 1Technical University of Munich, Department of Computer Science, Germany 2Mila Quebec AI Institute & McGill University, School of Computer Science, Canada 3King’s College London, Department of Informatics, United Kingdom {phillip.schneider, tim.schopf, juraj.vladika, matthes}@tum.de mikhail.galkin@mila.quebec elena.simperl@kcl.ac.uk 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 represen- tation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experienc- ing 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 comprehen- sive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contribut- ing to closing this gap, we systematically ana- lyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contribu- tions. 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. 1 Introduction Knowledge acquisition and application are inher- ent to natural language. Humans use language as a means of communicating facts, arguing about de- cisions, or questioning beliefs. Therefore, it is not surprising that computational linguists started al- ready in the 1950s and 60s to work out ideas on how to represent knowledge as relations between con- cepts in semantic networks (Richens, 1956; Quil- lian, 1963; Collins and Quillian, 1969). More recently, knowledge graphs (KGs) have emerged as an approach for semantically repre- senting knowledge about real-world entities in a machine-readable format. They originated from research on semantic networks, domain-specific ontologies, as well as linked data, and are thus not an entirely new concept (Hitzler, 2021). Despite their growing popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. Although prior work has al- ready attempted to define KGs (Pujara et al., 2013; Ehrlinger and Wöß, 2016; Paulheim, 2017; Färber et al., 2018), the term is not yet used uniformly by researchers. Most studies implicitly adopt a broad definition of KGs, where they are understood as "a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent rela- tions between these entities" (Hogan et al., 2022). KGs have attracted a lot of research attention in both academia and industry since the introduc- tion of Google’s KG in 2012 (Singhal, …

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