KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Reese, Justin; Unni, Deepak; Callahan, Tiffany J.; Cappelletti, Luca; Ravanmehr, Vida; Carbon, Seth; Shefchek, Kent; Good, Benjamin M.; Balhoff, James P.; Fontana, Tommaso; Blau, Hannah; Matentzoglu, Nicolas; Harris, Nomi L.; Muñoz-Torres, Monica; Haendel, Melissa; Robinson, Peter N.; Joachimiak, Marcin P.; Mungall, Christopher J.

doi:10.1016/j.patter.2020.100155

Cited by 62 publications

(46 citation statements)

References 29 publications

(23 reference statements)

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“…In response to the pandemic, many research groups have started projects to understand the SARS-CoV-2 virus life cycle and to find solutions. Examples of the numerous projects include outbreak.info [2], Virus Outbreak Data Network (VODAN) [3], CORD-19-on-FHIR [4], KG-COVID-19 knowledge graph [5], and the COVID-19 Disease Map [6]. Many research papers and preprints get published every week and many call for more Open Science [7].…”

Section: Introductionmentioning

confidence: 99%

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Waagmeester

Willighagen

et al. 2021

BMC Biol

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Background Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons.” Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. Results As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Conclusions Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

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Section: Introductionmentioning

confidence: 99%

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Waagmeester

Willighagen

et al. 2021

BMC Biol

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“…The second paper, by Reese et al [ 33 ], is a framework for producing KGs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface. For example, a drug repurposing application would make use of protein data linked with approved drugs, while a biomarker application could utilize data on gene expression linked with pathways.…”

Section: Resultsmentioning

confidence: 99%

“…Three papers were included in this application group: Chen et al [ 30 ], Reese et al [ 33 ], and Ostaszewski et al [ 34 ]. Chen et al [ 30 ] discussed four experiments in their paper: identifying experts on coronavirus topics for building collaborations, named entity recognition with BioBERT, co-occurrence frequency-based KG, and cosine similarity-based KG.…”

Section: Discussionmentioning

confidence: 99%

“…Reese et al [ 33 ] downloaded data from multiple siloed and incompatible data sources before converting and combining them using the KGX interchange format. It is unclear why KGX is not a more widely used format: it was not mentioned in any of the other papers included in this review, despite offering the advantage of combining features of RDF and property graphs.…”

Section: Discussionmentioning

confidence: 99%

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Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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“…Knowledge graph, a graph-based machine-readable data structure, was originally developed to describe interactions between entities and has recently been used as a network-based knowledge discovery tool for understanding COVID-19 and finding a therapy for the disease [ 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding

Yang

Sohn

2021

IJERPH

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A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.

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KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response

Cited by 62 publications

References 29 publications

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding

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