Real Estate Knowledge Graph

This is an example of a Knowledge Graph or Metadata Catalog where several different data sources are linked together in a Spanner Graph Database.

Graph backed Knowledge Graphs are optimized for answering complex questions that would be either too complex or slow using standard SQL.

Buiding a Real Estate Property Graph with Google Cloud Spanner

We are building the following tables of data about real estate properties, proerty owners, credit reports, and companies.

The data is related to one another through the following graph relationships

It is possible to combine Vector search, Full Text Search and Graph queries to get the most relevant results all from a single SQL compatable database that scales globally

Prerequisites

• Google Cloud SDK
• Python 3 or greater and Python PIP
• Working Google Cloud CLI
• A Google Cloud project
• make

Create the Spanner Instance

gcloud auth application-default login
make instancecreate

Setup Python environment

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt 

Generate your Property Graph DDL

export gcp_project_id="YOUR_PROJECT_ID"
make genschema

Create the database with the necessary DDL

make loadschema

Load some sample data into the database

python ./generate_data.py

Check out some of the queries you can run

Queries