A knowledge graph is really just a projection of structured data from disparate sources into a common schema. Take a bunch of tables and covert each row into a

A knowledge graph is really just a projection of structured data from disparate sources into a common schema. Take a bunch of tables and covert each row into a tuple (rowkey, columnName, value). Now take the union of all the tables. ^ knowledge graph That’s it…but it’s not very useful yet. It becomes more useful if you apply a shared ontology during the import—ie translate all the columns into the same namespace. Suppose we had a “contacts” table with columns {“first name”, “last name”, …} and a “events” table with columns {“participant given name”, “participant family name”, …} — basically you need to unify the word you use to describe the concept “first name”/“given name”/whatever across all sources. This can be cool/useful because you now only need one table (of triples) to describe all your structured data, but it’s also a pain because you may need to perform lots of self-joins or recursive queries to recover your data in order to do useful things with it. The final table has a very simple “meta” schema, and you erase the schema from each individual source so you can push the schema into the data.