#4250 Python Workflow for Knowledge Graph in RDF/SPARQL/Pyvis
#4250 Python Workflow for Knowledge Graph in RDF/SPARQL/Pyvis #4250 I want to develop the following Python workflow: CSV -> RDF, RDF Schema -> SPARQL Query Builder, Tkinter GUI to query RDF -> Pyvis to visualize graph from query results This is a classic Knowledge Graph engineering stack. To make this work seamlessly, you’ll want to use rdflib as your backbone—it handles the parsing, schema management, and SPARQL execution all in one go. Here is a conceptual breakdown and a starter implementation to get your workflow running. 1. The Core Engine: CSV to RDF To convert CSV to RDF, you map columns to URIs and Literals. Using a schema (RDFS) allows you to define classes and properties, ensuring your graph isn't just "flat" data. import pandas as pd from rdflib import Graph, Literal, RDF, URIRef, Namespace from rdflib.namespace import RDFS, FOAF # Initialize Graph and Namespace g = Graph() EX = Namespace( "http://example.org/" ) g.bind( "ex...