I will go over those resources you suggested. I would really appreciate help with my specific case too.
I'm taking inspiration from the Panama papers and the wiki-data process. Im trying to apply a variant of clean_wiki which took Wikipedia and turned it into wiki-data, and use it on news articles with meta data for the articles as property values.
The goal is to break an article into "facts", then merge multiple related articles into one graph with the duplication of facts from multiple sources "confirming" and showing trends across the field.
As of now im using the Tutorial: Build a Knowledge Graph using NLP and Ontologies as a starting point.
Like the tutorial my test set is currently set so each node is a full article with metadata as property values. The next step is to reduce it further into "facts". I was going to use this snippet as a starting point for getting the NLP extraction. I am not trying to merge in the ontology portion so i was working on how to simplify it down when i decided to look for examples.
Thank you for the help
MATCH (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"})
CALL apoc.nlp.gcp.entities.stream(a, {
nodeProperty: 'body',
key: $key
})
YIELD node, value
SET node.processed = true
WITH node, value
UNWIND value.entities AS entity
WITH entity, node
WHERE not(entity.metadata.wikipedia_url is null)
MERGE (page:Resource {uri: entity.metadata.wikipedia_url})
SET page:WikipediaPage
MERGE (node)-[:HAS_ENTITY]->(page)
