How we query Wikipedia’s Knowledge Graph (DBpedia) in SQL using timbr

Transform existing databases into Knowledge Graphs
Semantic Web Inference and Relationships in SQL
A summary of the DBpedia ontology by inheritance and relationships
A summary of the concept person
Insights into the concept person relationships
Querying in timbr’s semantic SQL
Querying in SPARQL
Querying in standard SQL
Querying in timbr’s semantic SQL via Zeppelin pyspark interpreter

So, how do we start? EASY.

Every query starts with a question, then we follow five simple steps to get the answers.
Q: Find administrative regions, where their leader wasn’t born in the same region

A new query using timbr-SQL answering our question
A snippet from timbr’s query results

What else can we achieve with our data using timbr?

We can get all relationships between two concepts, understand their direction (is_inverse) and names (to use in our SQL query), as well as understand the relationship type, whether it be transitive, many-to-many or perhaps one-to-many:

Get all relationships between concepts person and place
A Snippet of the relationships between concepts person and place
dtimbr schema example
dtimbr schema result
etimbr schema example
etimbr schema result
Concept work and its derived concepts in timbr’s Ontology Modeler
The timbr BLI platform

Conclusion

We now understand the DBpedia knowledge graph with its concepts and relationships a lot better. We also learned in a nutshell how to query connected data using timbr’s semantic SQL.
I hope that the experience of going through timbr’s new platform encourages you to learn more about your data and how it’s all connected.

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