This tutorial provides an introduction to building knowledge graphs by using open source libraries in Python. We’ll introduce the key concepts and abstractions, discuss engineering trade-offs, and emphasize hands-on coding exercises.
The coding exercises are based on progressive examples based on managing the content for a website, which illustrate how to integrate the use of:
Plus related use of pandas, numpy, matplotlib, pylev, and other libraries that help with building and analyzing KGs in open source Python.
We will work in Jupyter notebooks, available from a public repository on GitHub, which can be run locally. Semantic technologies used within these examples include OWL, FOAF, XSD (for literals), and some SKOS, which are represented in Turtle and JSON-LD formats.
Participants are encouraged to ask questions throughout the lectures, exercises, and during breaks.