A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022)

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.

## introduction knowledge acquisition and application are inherent to natural language. humans use language as a means of communicating facts, arguing about decisions, or questioning beliefs. therefore, it is not surprising that computational linguists started already in the 1950s and 60s to work out ideas on how to represent knowledge as relations between concepts in semantic networks @xcite @xcite . more recently, knowledge graphs (kgs) have emerged as an approach for semantically representing 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 @xcite . 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 already attempted to define kgs @xcite @xcite , 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 relations between these entities" @xcite . kgs have attracted a lot of research attention in both academia and industry since the introduction of google's kg in 2012 @xcite . particularly in natural language processing (nlp) research, the adoption of kgs has become increasingly popular over the past 5 years, and this trend seems to be accelerating. the underlying paradigm is that the combination of structured and unstructured knowledge can benefit all kinds of nlp tasks. for instance, structured knowledge from kgs can be injected into that of the contextual knowledge found in language models, which improves the performance in downstream tasks @xcite . furthermore, with the growing importance of kgs, there are also expanding efforts to construct new kgs from unstructured texts. ten years after google coined the term knowledge graph in 2012, a plethora of novel approaches has been proposed by scholars. therefore, it is important to assemble insights, consolidate existing results, and provide a structured overview. however, to our knowledge, there are no studies that offer an overview of the whole research landscape of kgs in the nlp field. contributing to closing this gap, we performed a comprehensive survey to analyze all research performed in this area by classifying established topics, identifying trends, and outlining areas for future research. our three main contributions are as follows: task taxonomy of knowledge graphs in natural language processing knowledge graph construction knowledge graph reasoning knowledge extraction knowledge acquisition knowledge application natural language understanding natural language generation attribute extraction entity extraction relation extraction knowledge integration entity alignment entity linking ontology construction entity classification error detection knowledge graph embedding link prediction relation linking relation classification natural language inference semantic parsing semantic search semantic similarity text analysis text classification data-to-text generation machine translation question generation text generation text summarization augmented language models conversational interfaces question answering triple classification 1. we systematically extract information from 507 included papers and report insights about tasks, research types, and contributions. our survey sheds light on the evolution and current research progress regarding kgs in nlp. although we cannot achieve complete coverage of all relevant papers on this topic, we aim at providing a representative overview that can help both nlp scholars and practitioners by offering a starting point in the literature. moreover, our multifaceted analysis can guide the research community in closing existing gaps and finding novel ways how to combine kgs with nlp. ## related work related literature that includes both kgs and nlp seems to be relatively scarce. most survey papers focus either only on kgs or only on nlp. in their broad introduction to kgs, @xcite point out that existing surveys on kgs tend to revolve around specific aspects of kgs, most commonly their construction and embedding. such surveys with a kg focus usually bring up nlp only in the context of employed nlp methods, like information extraction, being used to populate and refine graphs @xcite . other surveys on kgs mention some downstream applications of kgs for nlp tasks, such as for con-structing augmented language models, question answering over knowledge bases (kbqa), or recommender systems @xcite . as noted previously, related work that includes both kgs and nlp strictly focus on a specific application or task. for example, @xcite provide an overview on applying relational world knowledge from kgs to augment large contextual language models. other surveys on specific applications include kg reasoning @xcite , biomedical kgs @xcite , and the task of kbqa @xcite . the survey on graphs in nlp by @xcite covers only smaller graphs such as dependency graphs and dialogue trees. even though it does not include kgs, the survey concludes that graphs are a powerful representation formalism and how nlp tasks can benefit from harnessing the potential of data presented in graph structures. to the best of our knowledge, this is the first survey covering a wide spectrum of techniques, methods as well as applications of kgs within the nlp research field. ## method to achieve our objective of providing a thorough overview of the research landscape, we conducted a systematic mapping study following the process defined by @xcite . its three main steps are explained in the next subsections. ## results in this chapter, we report the results of the data extraction process. it is arranged into subsections according to the formulated rqs. ## discussion the observations of our comprehensive survey reveal several insights. it is important to situate these findings with respect to related work and industry reports in the artificial intelligence (ai) field. since the first publications in 2013, researchers worldwide have paid increasing attention to study kgs from a nlp perspective, especially in the past five years. this observed growth in research interest is in line with the kg survey of @xcite . we identified china and the united states as the most active countries shaping the research landscape, which is to be expected considering both countries regularly claim the top ranks in the popular "ai index report" from stanford university @xcite . the report further highlights a soaring ai investment in the health domain. the latter was also the most dominant domain in our results (see figure 3 ). however, research in the health domain has to be considered critically, since these works compare poorly to other domains regarding reproducibility metrics, such as dataset and code accessibility @xcite . the frequency of tasks in our survey greatly varies, as reflected in ## limitations although we employed a rigorous study design and paid careful attention to executing each search and analysis step, our study is subject to limitations. given the restriction to one search string and six databases, there should be some relevant publications that we did not retrieve. this is the case for studies that did not mention our search terms in title, abstract, or keywords. to mitigate the risk of incompleteness, we chose common databases with a large number of publications in the examined research area. further, we performed a preliminary search to optimize the completeness of results. whenever possible, we replaced missing keywords with index keywords from the source database. moreover, the screening for relevant studies depends on the personal assessment of the researchers, which can bias the study selection. as a countermeasure, we defined selection criteria for the inclusion and exclusion of studies. during the study selection, two researchers assessed of selection criteria in parallel and discussed contradicting decisions until they reached a consensus to mitigate subjective bias. the accuracy of the classification results constitutes another threat to the validity of our study. data extraction bias may negatively affect the accuracy of the classification results. to mitigate this risk, the authors regularly discussed the used classification schemes and assigned labels to establish a common understanding of each class. in addition, we calculated cohen's kappa coefficient to quantify the reliability of the inter-annotator agreement. ## conclusion recent years have witnessed a rising prominence of kgs in nlp research. despite the rapidly growing body of literature, until now, no study has been published that summarizes the progress so far. to provide an overview of this maturing research area, we performed a multifaceted survey of tasks, research types, and contributions. our findings show that a large number of tasks concerning kgs in nlp have been studied across various domains, including emerging topics like knowledge graph embedding or augmented language models. however, we observed a lack of secondary research and evaluations in practice, both of which are crucial to reflect the major scientific progress of the field as a whole. our study lays the grounds for further research in this direction.