So I am new to the world of the semantic web, RDF's and Ontologies How does the process of creating knowledge graphs work? Say I want to create knowledge graphs about a specific team and link everything about it the players, trophies and everything how will it go? Do I first scrape data about the team? Do I convert from CSV to RDF triples. And where do Data Science, NLP and Machine Learning fall into all this?
Ok, there are a few components to this. I will take each in turn.
Part 1:
So I am new to the world of the semantic web, RDF's and Ontologies How does the process of creating knowledge graphs work? Say I want to create knowledge graphs about a specific team and link everything about it the players, trophies and everything how will it go?
Some high-level steps:
players
and an instance could be a player
in your team. The players
class could be linked to the trophies
class to show which players have won trophies. This guide might prove usefulSPARQL INSERT
operation. There are also more complex frameworks for turning data into knowledge graphs.However, given your use-case I would ignore everything I've written above as this sounds like a solved problem. See, the beauty of RDF is that there is a big community of open data and shared ontologies. It is likely the graph you want to create could at least partially be sourced from existing public graphs which already aggregate and crowd-source data from the web.
See the SPARQL endpoints:
Part 2
Do I first scrape data about the team? Do I convert from CSV to RDF triples.
I would avoid scraping if you can, and try to rely on the above public graphs that already exist. However, scraping is an option if required.
Part 3
And where do Data Science, NLP and Machine Learning fall into all this?
Increasingly knowledge graphs are being used as part of machine learning workflows. There are a few reasons for this: