Extract relations from sentences in the form of triples
Find relations in the text with SPARQL queries
Create new visualizations and dashboards
This is a rest endpoint for creating OpenIE Triples extractions. "The Open IE system runs over sentences and creates extractions that represent relations in text. There are many binary relations in a sentence that can be expressed as a triple (A, B, C) where A and B are arguments, and C is the relation between those arguments. Since Open IE is not aligned with an ontology, the relation is a phrase of text." Input your own sentence and extract triples that can be used to build your own knowledge graph.
This knowledge graph is built on top of the OpenIE triples extractions. Additionally, instances of clinical and biomolecular entities have been tagged in the text, allowing the user to query for relations and entities. You can post SPARQL queries to the graph. For a list of prefixes of the resource mappings and predicates see the documentation Example queries can be found here
Create your own visualizations and dashboards of CORD-19 using Kibana.
Free text search of the full CORD-19 corpus. Filter and facet on various aspects of the data such as journal, publication date, mentions of named entities such as Genes, Proteins, Anatomical Sites, etc. Named entities were extracted from the text using Apache cTakes and SciSpacy Named Entity Recognition models.
The COVID-19 severity map portrays the correlation between weather and the severity of the COVID-19 pandemic across various locations in the world. The colouring of the locations depicts the Koppen-Geiger climate zone (starting from polar climates and ending with tropical/megathermal climates). The green markers within each of those locations depict the severity of COVID-19, calculated using the deaths, cases, total number of mentions, number of papers that mention the location, as well as number of times death is mentioned alongside that location in a paper. Explore the interactive map.