Databases Graph Databases pythonSee in schedule: Tue, Jul 27, 13:15-14:45 CEST (90 min)
Storing data in a tabular format is not always ideal. Taking advantage of strong data in knowledge graphs can make handling complex data structure possible and data visualization easier. In this workshop, you will get all the basics to start modelling data in the terms of triples and building schemas of a knowledge graph.
For whom is your Workshop *
Data scientist, engineers and researchers who have no prior experience in knowledge graph data modelling. In this workshop, we will start from the fundamentals - learning how to think in terms of triples to describe relations of different data objects. If your work involves data analysis, data management, data collaboration or anything data-related, this is a workshop for you to have a brand new insight into how data should be represented and stored.
Short Format of your Workshop *
Overview-5 min, Lecture - 30 mins, Breaks- 10 minutes, Hands-on training - 40 mins, Closing - 5 mins
Workshop Agenda *
Overview-5 min In this session, we will go through the workshop structure, introduce TemrinusDB - the open-source tool that we use and pre-flight check to make sure everyone’s set up is ready.
Lecture - 30 mins In this session, through slides and presentation, we will go through the fundamental construct of a knowledge graph: - What is triple - What are objects, documents, and other elements in a knowledge graph - Different types of properties Then we will show an example of how data that was represented in a relational database (tables joined with keys) can be reconstructed as a knowledge graph and the elegance of doing so.
Breaks- 10 minutes A short break, overrun buffer and answering questions.
Hands-on training - 40 mins At the start of this session, there will be a short tour and demo of how they can build a knowledge graph schema with the schema builder in TerminusDB. (10 mins)
Then attendees will be given a dataset that is represented in tables and they will need to apply what they learnt in the lecture and construct a schema that works the best for it. They are encouraged to ask questions during this session. (20 mins)
Finally we will be building the same schema with the Python client. (10mins)
Closing - 5 mins In this session, we will conclude what the attendee has achieved. We will also provide suggestions if they would like to continue learning how to work with knowledge graphs and acquire related skills - for example, using Python client to manage data in TerminusDB programmatically.
What is required from attendees *
A computer with stable internet connection; TerminusDB Desktop App or Docker image (a.k.a TerminusDB Bootstrap) which you can download from https://terminusdb.com/hub/download (FREE); Python client for TerminusDB (require Python >=3.7); An opened mind and ready to learn something new
What Attendees will Learn *
By the end of the workshop, you will be able to think like a knowledge graph expert and construct a proper schema to store your data in a knowledge graph format. You will acquire the skills that you need to build knowledge graphs in TerminusDB - an open-source graph database that enables revisional control and collaborations.
Course Benefits *
You will have learnt a new skill set that may assist you in your project in data science or research. You will have a new tool that you can better model your data and collaborate with others. Also, you gain all the prerequisites to use WOQL - a query language for knowledge graph and the TerminusDB Python client to manage, manipulate and visualize data in your knowledge graph.
Type: Training (180 mins); Python level: Beginner; Domain level: Beginner
After doing research in theoretical physics, Cheuk has transferred her analytical and logical skills in natural science and built a career in data science. Cheuk has been a Data Scientist working in the travel business, a consultancy and a global bank. She now brings her knowledge in data and passion for the tech community into TerminusDB(https://terminusdb.com/) as the developer relations lead.
Cheuk constantly contributes to the open-source community by giving AI and deep learning workshops and organize sprints to encourage contributions. Cheuk has also been a guest speaker at various conferences and co-organize AI Club for Gender Minorities to support Diversity and Inclusion. More about Cheuk's community work and projects: https://cheuk.dev