07-07, 13:30–17:30 (US/Pacific), Room 317
Ontologies provide a powerful way to structure knowledge, enable reasoning, and support more meaningful queries compared to traditional data models. Recently, interest in ontologies has resurged, driven by advancements in language models, reasoning capabilities, and the growing adoption of platforms like Palantir Foundry.
In this hands-on tutorial, participants will explore ontology development across multiple domains using a variety of Python-based tools such as rdflib, Owlready2, SWI-Prolog, PySpark, Pandas, NetworkX, and SciPy. They will learn how ontologies facilitate semantic reasoning, improve data interoperability, and enhance query capabilities.
Additionally, attendees will build a rudimentary reasoning engine to better understand inference mechanisms.
The tutorial emphasizes practical applications and comparisons with conventional data representations, making it ideal for researchers, data engineers, and developers interested in knowledge representation and reasoning.
Ontologies make data more structured, meaningful, and machine-readable. This tutorial will guide participants through building and reasoning over ontologies in various domains, such as movies, music, healthcare, finance, and construction. We will use Python libraries like rdflib, Owlready2, PySpark, Pandas, NetworkX, and SciPy to model domain-specific concepts, relationships, and constraints. We will also explore how ontology-driven reasoning enhances queries beyond standard data representation approaches.
This tutorial will cover:
- Introduction to Ontologies: Basics of OWL, RDF, and SPARQL.
- Building Ontologies: Developing domain-specific models with Python tools.
- Queries and Reasoning: Writing SPARQL queries and applying inference.
- Comparison with Other Models: Evaluating ontologies against relational and graph-based models.
- Developing a Rudimentary Reasoning Engine: Implementing a simple rule-based system in Python.
- Hands-on Development: Creating ontologies in up to ten domains, including:
- Movies
- Music
- Supply Chain
- Property & Casualty Insurance
- Construction
- Manufacturing
- Stock Market / Equities Trading
- Healthcare / EHR + Claims
- Pharmaceutical Supply Chain (+ Bonus: Ontology Matching)
- EPCC / LEMS / PMBoK-based large construction projects
Target Audience:
Designed for anyone interested in knowledge representation, semantic reasoning, and ontology-driven data modeling. Some familiarity with Python would be needed, also some familiarity with data processing tools like Pandas would be helpful; prior ontology knowledge is not needed.
Jupyter Lab, Anaconda distribution (optional)
Prerequisites –Familiarity with Python, some idea of Pandas or Spark, but not mandatory.
Shaurya Agarwal has been tinkering with Data, Cloud technologies, Machine Learning, Data Science and now GenAI for over 21 years. Now a Director with PwC India, Shaurya brings his expertise and experience to solve critical problems across a very wide set of domains.