Pizza Knowledge Graph

This was the coursework for the Semantic Web-tech and Knowledge Graphs Coursework. I was not enrolled in this module. But decided to do this later.

The specification is here and the data is here

The final class hierarchy:

Ontology Modelling

The original ontology/Knowledge Graph is stored in the ontologyModelling folder.

Tabular Data to Knowledge Graph

This converts the CSV data to a Knowledge Graph. To run use:

python3 -m tabularDataToKG

SPARQL and Reasoning

To run reasoning use:

python3 sparqlAndReasoning/inference.py

Restaurants which sell Margherita pizza

Pizza Bianca Restaurants CSV

python3 sparqlAndReasoning/pizza_bianca_restaurants.py

Average price of a Margherita Pizza

Average price of a Margherita pizza: $12.05

python3 sparqlAndReasoning/average_price_margherita.py

Number of restaurants

Number of Restaurants CSV

python3 sparqlAndReasoning/number_restaurants.py

Restaurant with no postcode

Restaurants missing postcode csv

python3 sparqlAndReasoning/restaurants_missing_postcode.py

Ontology Alignment

To run use:

python3 ontologyAlignment/alignment.py

Recreate the data using:

python3 -m ontologyAlignment.recreate_data

Then load the data into GraphDB, setting up the repository using the settings given in the file. Add all the namespaces from the turtle file. Then run the query in the SPARQL file.

Class Relationships:

Ontology Embeddings

Here I used OWL2Vec to create various models from my pizza Knowledge Graph.

Vector Similarity

Clustering

There did not seem to be much difference in changing the number of clusters:

Pizza Ontology Model:

Pizza Data Model:

Pizza Data New Seed Model

Files

• data.ttl: the data in the original ontology
• dataPostAlignment.ttl: the data in the aligned ontology
• mainAlignedOntology.tll: the merged (and modified) Pizza.owl ontology and my ontology
• ontology.owl: my pizza ontology
• Pipfile: the various Python dependencies

Documentation

To run use:

pipenv run dynamic_docs

To save use:

pipenv run save_docs