Toward a Coronavirus Knowledge Graph

Zhang, Peng; Bu, Yi; Jiang, Peng; Shi, Xiaowen; Lun, Bing; Chen, Chongyan; Syafiandini, Arida Ferti; Ding, Ying; Song, Min

doi:10.3390/genes12070998

Cited by 8 publications

(11 citation statements)

References 55 publications

(59 reference statements)

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“…Knowledge graphs have been used for many applications, including drug discovery and repurposing, target detection, and prediction (Alshahrani and Hoehndorf 2018;Moon et al 2021;Zheng et al 2021;Alves et al 2021). Other applications include integration and analysis of heterogeneous COVID-19 data (Steenwinckel et al 2020;Cernile et al 2021;Reese et al 2021;Domingo-Fernández et al 2021;Zhang et al 2021;Ostaszewski et al 2021;Chen et al 2021), oncology research (Zhu et al 2023;Zhao et al 2023;Jha et al), and gene-disease associations (Choi and Lee 2021;Alves et al 2021) among many others. The unifying intent in these projects is the meaningful The data loading pipeline contains four major steps (Figure 2): (1) Data selection and modeling, (2) Cleaning and preparing data, (3) Executing the UBKG "OWLNETS" pythons scripts and (4) Import using the Neo4j Bulk Import tool.…”

Section: Introductionmentioning

confidence: 99%

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Stear

Ahooyi

Vasisht

et al. 2023

Preprint

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The use of biomedical knowledge graphs (BMKG) for knowledge representation and data integration has increased drastically in the past several years due to the size, diversity, and complexity of biomedical datasets and databases. Data extraction from a single dataset or database is usually not particularly challenging. However, if a scientific question must rely on analysis across multiple databases or datasets, it can often take many hours to correctly and reproducibly extract and integrate data towards effective analysis. To overcome this issue, we created Petagraph, a large-scale BMKG that integrates biomolecular data into a schema incorporating the Unified Medical Language System (UMLS). Petagraph is instantiated on the Neo4j graph platform, and to date, has fifteen integrated biomolecular datasets. The majority of the data consists of entities or relationships related to genes, animal models, human phenotypes, drugs, and chemicals. Quantitative data sets containing values from gene expression analyses, chromatin organization, and genetic analyses have also been included. By incorporating models of biomolecular data types, the datasets can be traversed with hundreds of ontologies and controlled vocabularies native to the UMLS, effectively bringing the data to the ontologies. Petagraph allows users to analyze relationships between complex multi-omics data quickly and efficiently.

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Section: Introductionmentioning

confidence: 99%

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Stear

Ahooyi

Vasisht

et al. 2023

Preprint

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“…We implement the COVID-19 KG as a case study as a COVID-19 KG could potentially play an important role for doctors, policymakers, epidemiologists and other domain experts currently trying to gain deeper insight into the crisis (Kejriwal, 2020). Even though several COVID-19 KGs have been constructed based on different resources (Wang et al , 2021a; Wise et al , 2020; Domingo-Fernández et al , 2021; Chen et al , 2020; Chatterjee et al , 2021), each of them is developed with a different purpose. For example, the COVID-19 literature KGs built by Wise et al (2020), Wang et al (2021a), Domingo-Fernández et al (2021) and Chen et al (2020) were applied for literature search, drug repurposing, pathophysiology discovery and multipurpose, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Even though several COVID-19 KGs have been constructed based on different resources (Wang et al , 2021a; Wise et al , 2020; Domingo-Fernández et al , 2021; Chen et al , 2020; Chatterjee et al , 2021), each of them is developed with a different purpose. For example, the COVID-19 literature KGs built by Wise et al (2020), Wang et al (2021a), Domingo-Fernández et al (2021) and Chen et al (2020) were applied for literature search, drug repurposing, pathophysiology discovery and multipurpose, respectively. In addition, some of them contained a limited number of entities and relations (Domingo-Fernández et al , 2021; Chen et al , 2020), others were not rigorously validated (Wang et al , 2021a; Chen et al , 2020), raising a data-quality issue.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the COVID-19 literature KGs built by Wise et al (2020), Wang et al (2021a), Domingo-Fernández et al (2021) and Chen et al (2020) were applied for literature search, drug repurposing, pathophysiology discovery and multipurpose, respectively. In addition, some of them contained a limited number of entities and relations (Domingo-Fernández et al , 2021; Chen et al , 2020), others were not rigorously validated (Wang et al , 2021a; Chen et al , 2020), raising a data-quality issue.…”

Section: Introductionmentioning

confidence: 99%

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Construction and evaluation of a domain-specific knowledge graph for knowledge discovery

Nguyen

Chen

et al. 2023

IDD

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Purpose This study aims to evaluate a method of building a biomedical knowledge graph (KG). Design/methodology/approach This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed. Findings With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG. Originality/value The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

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“…However, there are only 10 entity types and 9484 facts within this KG. Coronavirus Knowledge Graph [ 22 ] only has 27 relation types. The CovidGraph project [ 23 ] built a COVID-19 graph that stores publications, case statistics, and molecular data in a Neo4j database, which enables exploring the underlying knowledge for finding specific genes, authors, articles, patents, proteins, existing treatments, and medications relevant to the entire family of coronaviruses.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

Wang¹,

Du²,

Qi³

et al. 2022

JMIR Med Inform

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Background With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. Objective To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. Methods In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. Results OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. Conclusions A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.

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Toward a Coronavirus Knowledge Graph

Cited by 8 publications

References 55 publications

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

Construction and evaluation of a domain-specific knowledge graph for knowledge discovery

Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

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