![]() presented in an interconnected, structured, and manageable way. The audience was able to see all Netflix’ data on movies, shows, actors, directors, countries etc. Once all the data was loaded we had a ‘big reveal’, through the Data Graphs explorer which allows you to navigate and visualise your knowledge graph. Taking individual CSV files prepared from the Kaggle Netflix data we populated our domain model one concept class at a time, and let Data Graphs stitch them together with identifiers that we had predefined. ![]() We built out the classes and their relationships and then finally we have a visual view of our domain model, replicated within Data Graphs. Then using Data Graphs’ schema builder we can replicate our domain model. The first step was to create datasets and concept classes, which is effectively our project canvas. We started by showing the domain model that we wanted to replicate and then began the process of building the knowledge graph. We took attendees through a step by step process of creating a knowledge graph in just 12 minutes with Data Graphs, using the open source data that Netflix has made available on Kaggle. Often companies that have a knowledge graph will have neglected their information management and the data quality is poor. Your knowledge graph is only as good as your domain model and this must be made up of well curated data. Many companies buy the knowledge graph technology, overlooking the essential information management side of things. Many of the knowledge graph initiatives that we see are technology-driven. There's a whole suite of technologies which are generally new to most organisations, they don't have this expertise internally around RDF and semantics. One of our main objectives for this talk was to show that we believe Data Graphs has just the right amount of semantics to demonstrate how we can lower the barrier of entry for knowledge graphs. The core question then is: ‘How much semantics is enough?’. If you start using an enterprise graph database and RDF, the semantics can get complicated quickly and it can be easy to get bogged down in a lot more complexity than you actually need. Common Knowledge Graph Challengesīefore we started with the process of building the knowledge graph, we wanted to highlight three main challenges that previous clients had raised time and time again when faced with this task: Our main aim therefore with this presentation was to show the amazing capabilities of our own knowledge graph product Data Graphs, and how quickly it helps you create a knowledge graph for a domain. We assumed that our audience were people who were either interested or actively working on knowledge graphs and feeling their way through the problem space. Here’s an overview of our objectives for this talk, and a summary of some of the things we spoke about. Last week we presented a talk at the online Knowledge Graph Forum hosted by Ontotext.
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