BioPropaPhenKG on Online Newspapers and Medical Articles

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in https://zenodo.org/records/10911980. The code used for this use case can be found in https://github.com/Gabriel382/DDPF-Health-Risks . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, Aylien (data only from November of 2019) and CORD-19 (data only from December of 2019). 

To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command: 

docker run --interactive --tty --rm \    --publish=7474:7474 --publish=7687:7687 \    --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\    neo4j:4.4.3 \neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force