COVID -19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021)

Abstract: To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.

## introduction practical progress at combating covid-19 relies heavily on effective search, discovery, assessment, and extension of scientific research results. however, clinicians and scientists are facing two unique barriers in digesting these research papers. the first challenge is quantity. such a bottleneck in knowledge access is exacerbated during a pandemic when increased investment in relevant research leads to even faster growth of literature than usual. for example, as of april 28, 2020, at pubmed 3 there were 19,443 papers related to coronavirus; as of june 13, 2020, there were 140k+ related papers, nearly 2.7k new papers per day (see figure 1 ). the resulting knowledge bottleneck contributes to significant delays in the development 1 demo video: http://159.89.180.81/demo/ covid/covid-kg_demovideo.mp4 let us consider drug repurposing as a case study. 4 besides the long process of clinical trials and biomedical experiments, another major cause of the lengthy discovery phase is the complexity of the problem involved and the difficulty in drug discovery in general. the current clinical trials for drug repurposing rely mainly on reported symptoms in considering drugs that can treat diseases with similar symptoms. however, there are too many drug candidates and too much misinformation published in multiple sources. the clinicians and scientists thus urgently need assistance in obtaining a reliable ranked list of drugs with detailed evidence, and also in gaining new insights into the underlying molecular cellular mechanisms on covid-19 and the pre-existing conditions that may affect the mortality and severity of this disease. to tackle these two challenges we propose a new framework, covid-kg, to accelerate scientific discovery and build a bridge between the research scientists making use of our framework and clinicians who will ultimately conduct the tests, as illustrated in figure 2 . covid-kg starts by reading existing papers to build multimedia knowledge graphs (kgs), in which nodes are entities/concepts and edges represent relations and events involving these entities, as extracted from both text and images. given the kgs enriched with path ranking and evidence mining, covid-kg answers natural language questions effectively. with drug repurposing as a case study, we focus on 11 typical questions that human experts pose and integrate our techniques to generate a comprehensive report for each candidate drug. ## a case study on drug repurposing the answers to questions #5 and #11 are extracted based on the meta-data sections of research papers in scientific literature, including the author affiliation and acknowledgement sections. the answers for other questions are all extracted based on the knowledge graphs constructed and knowledge-driven question-answering method described above. as in our case studies, darpa biologists inquired about three drugs, benazepril, losartan, and amodiaquine, and their links to covid-19 related chemicals/genes as shown in figure 8 : bm1_00870 bm1_06175 bm1_16375 bm1_17125 bm1_22385 bm1_30360 bm1_33735 bm1_56245 bm1_56735 bm1_00870 bm1_06175 bm1_16375 bm1_17125 bm1_22385 bm1_30360 bm1_33735 bm1_56245 smp_067050 smp_067060 smp_085010 smp_085180 smp_103610 smp_105370 smp_158410 smp_158420 smp_179950 tsp_01409 tsp_02382 tsp_02383 tsp_03306 tsp_07747 tsp_10129 tsp_10493 tsp_11596 lman1 lman1l our kg results for many other drugs are visualized at our website foot_4 . we download new covid-19 papers from three application programming interfaces (apis): ncbi pmc api, ncbi pubtator api, and cord-19 archive. we provide incremental updates including new papers, removed papers and updated papers, and their metadata information at our website foot_5 . ## conclusions and future work we have developed a novel framework, covid-kg, that automatically transforms a massive scientific literature corpus into organized, structured, and actionable kgs, and uses it to answer questions in drug repurposing reporting. with covid-kg, researchers and clinicians are able to obtain informative answers from scientific literature, and thus focus on more important hypothesis testing, and prioritize the analysis efforts for candidate exploration directions. in our ongoing work, we have created a new ontology that includes 77 entity subtypes and 58 event subtypes, and we are building a neural ie system following this new ontology. in the future, we plan to extend covid-kg to automate the creation of new hypotheses by predicting new links. we will also create a multimedia common semantic space @xcite for literature and apply it to improve cross-media knowledge grounding and inference.