Abstract
Scientists are harnessing their multi-disciplinary expertise and resources to fight the COVID-19 pandemic. Aligned with this mindset, the Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the “COVID-19 Open Research Dataset” (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clinicians analyze clinical trials and make decisions. On top of this dataset, we provide several visualization and exploration tools based on the Corese Semantic Web platform, MGExplorer visualization library, as well as the Jupyter Notebook technology. All along this initiative, we have been engaged in discussions with healthcare and medical research institutes to align our approach with the actual needs of the biomedical community, and we have paid particular attention to comply with the open and reproducible science goals, and the FAIR principles.
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PICO is a framework to answer health-care questions in evidence-based practice that comprises patients/population (P), intervention (I), control/comparison (C) and outcome (O).
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BERT is a self-attentive transformer models that uses language model (LM) pre-training to learn a task-independent understanding from vast amounts of text in an unsupervised fashion.
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The intervention and comparison label are treated as one joint class.
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Inputs were tokenized with the BERT tokenizer, where one sub-word token has a length of one to three characters.
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Covid-on-the-Web dataset URI: http://ns.inria.fr/covid19/covidontheweb-1-1.
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ODC-By license: http://opendatacommons.org/licenses/by/1.0/.
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Covid Linked Data Visualizer can be tested at: http://covid19.i3s.unice.fr:8080.
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Dataframes are tabular data structures widely used in Python and R for the data analysis.
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Michel, F. et al. (2020). Covid-on-the-Web: Knowledge Graph and Services to Advance COVID-19 Research. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_19
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