COVID -19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature (2020)

Abstract: The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power www.cord19.aws and is publicly available.

## introduction the onset of the novel sars-cov-2 virus has emphasized the need to accumulate insights from large volumes of information. thousands of new scientific articles on the virus are being published weekly, leading to a rapid increase in the cumulative knowledge about the coronavirus disease . covid-19 has heightened the need for tools that enable researchers to search vast scientific corpora to find specific information, visualize connections across the data, and discover related information in the data. several covid-19 dedicated search engines have come online to address the need for information retrieval of scientific literature on the disease. search engines like sketch engine covid-19, sinequa covid-19 intelligent search, microsoft's cord19 search, and amazon's cord19 search use a variety of methodologies such as keyword search, natural language queries, semantic relevancy, and knowledge graphs. however, these engines return thousands of search results that overlook inherent relationships between scientific articles, such as subject topic and citations, and do not provide tools to visualize relationships, which is beneficial for knowledge discovery. in this paper, we construct the covid-19 knowledge graph (ckg) by extracting rich features and relationships of covid-19 related scientific articles and develop a document similarity engine that combines both semantic and relationship information from ckg. knowledge graphs (kgs) are structural representations of relations between real-world entities where relations are defined as triplets containing a head entity, a tail entity, and the relation type connecting them. kg based information retrieval has shown great success in the past decades @xcite . we construct the ckg using the cord19 open research dataset of scholarly articles @xcite . scientific articles, publication authors, author institutional affiliations, and citations form key relationships in the graph. further, we extract biomedical entity relationships and highly abstracted topics from the unstructured text of articles using amazon comprehend medical service and train a topic model on the corpus. by applying data normalization technologies we eliminate duplicate entities and noisy linkages. the resulting kg contains 336,887 entities and 3,332,151 relations. the ckg has been made publicly available to researchers with rapid "one-click" cloud deployment templates. 1 we introduce a document similarity engine that leverages both the semantic information of articles and the topological information from the ckg to accelerate covid-19 related information retrieval and discovery. we employ scibert @xcite , a pretrained nlp model, to generate semantic embeddings for each article. meanwhile, we utilize knowledge graph embedding (kge) @xcite and graph neural network @xcite technologies to generate embeddings for entities and relations of the ckg. finally, by combining judiciously the semantic embeddings and graph embeddings we use the similarity engine to propose top-k similar articles. the ckg and similarity engine are new additions to www.cord19.aws , a website using machine learning to help researchers search thousands of covid-19 related scientific articles using natural language question queries that has seen over 15 million queries across more than 70 countries. the ckg adds a graph-traversal ranking feature to search and the similarity engine powers the similarity-based recommended article system. to further demonstrate the quality of the ckg, we conduct a series of experiments analyzing the relations that form the core pillars of the graph. we first evaluate the ability of our methodology to capture the topic information in the text, and show that extracted topics align well with the subjects of scientific journals. we also perform link prediction analysis by extracting graph embeddings that validates the quality of the relations in the graph and demonstrates that we capture important topological information from the ckg. our analysis shows that the semantic embeddings and graph embeddings learn useful information and improve our ability to quantify similarity between articles. lastly, several motivating examples show that querying the ckg can extract actionable insights from scientific articles. to summarize, our contribution is fourfold: knowledge graph (ckg), by judiciously combining the inherent schema information from covid-19 scientific articles as well as the extracted biomedical entity relationships and topic information. ## ckg construction & curation ckg is a directed property graph where entities and relations have associated attributes (properties) and direction (directed). figure 1 illustrates the directed property graph structure for a small subgraph of ckg. in this section we describe the dataset used to construct the ckg, define the entity and relation types, detail ckg curation methods, provide summary statistics describing the graph, and detail the cloud infrastructure that drives ckg scalability. ## using ckg for article recommendations in this section we combine article semantic information with ckg topological information to quantify similarity between articles and construct a similarity-based recommendation system. ## analysis this section is organized into two parts presenting metrics and results evaluating the work done in sections 2 and 4 respectively. part one validates the construction and curation of the ckg by showing article topics align with common subject focuses of scientific journals and ckg relations are high quality. part two analyzes the results of the similarity engine demonstrating we can improve the quality of recommended articles using both semantic and topological information. 5.1 graph validation 5.1.1 topic model validation most journals have well defined topics. for example, journal of virology explores the nature of viruses and mainly focuses on related domains; journal of vaccine focuses on the field of vaccinology. to evaluate our topic model, we summarise the generated topics from papers in the ckg belonging to these two journals in figure 4 . it can be seen that the generated topics of papers from journal of virology, e.g., virology, genomics and lab-trials-human, are highly related to virology. the generated topics of papers from journal of vaccine, e.g., vaccines-immunology, are highly related to vaccinology. ## conclusion in this paper we construct a covid-19 knowledge graph from the cord-19 dataset and demonstrate how researchers and policy makers can extract timely information to answer key scientific questions on covid-19 from a corpus of scientific articles. to further facilitate efforts we employ machine learning entity detection models to extract medical entities and relationships. with the help of medical professionals we add global topic information that forms additional medical relationships in the ckg. we train kge models using ckg relations to obtain paper embeddings capturing topological isomorphic and semantic information for the application of similar paper retrieval on www.cord19.aws . future work may include further enhancements to ckg information retrieval capabilities such as: expanding biomedical entity extraction using biomedical concept annotators like pubtator foot_1 , re-training rgcn models with additional entity and relation attributes, and incorporating additional kgs into the ckg e.g. covid-19 drug repurposing graphs @xcite .