Notes about Ontopic's approach to Data Integration, Mapping, and building knowledge graphs

Ontopic has a distinctive approach to data integration, which we would like to share through short and easily readable articles. We kept the number of specific terms to a minimum and created a glossary for the few terms we think are almost unavoidable.

These articles are more of general nature, we also have a section for technical documentation specific to Ontopic products.

Create Flexible Enterprise Knowledge Graphs with Virtualization and Apache Iceberg

Apache Iceberg is one of the most fascinating technologies regarding standardized access to large analytic tables. Combining Apache Iceberg with Virtual Knowledge Graph enables the creation of highly interoperable and easily queryable data architectures at the enterprise scale. It does it efficiently by leveraging the scalability of powerful cloud data sources, knowledge graphs' expressivity, and the agility of data virtualization.
 10 min

Make data in Apache Parquet Files AI ready with Virtual Knowledge Graphs

Large datasets, both within organizations and from public providers, are increasingly being distributed as Apache Parquet files. Using graph virtualization, they can be also made much more accessible to humans and LLMs alike.
 10 min

How to deploy your knowledge graph in a graph database with Ontop

Many people build a knowledge graph by moving and transforming existing data into a graph database. This approach is also known as knowledge graph materialization. During the past decade, Ontop has become a reference open-source solution for materializing knowledge graphs from relational data sources in large organizations.In this article, we present two ways to materialize your knowledge graph using Ontop.
 10 min

What is a virtual knowledge graph (VKG), and what are its applications for business?

In today's data-driven world, businesses are constantly seeking new ways to integrate and leverage data from multiple sources. One approach that has gained popularity in recent years is the use of virtual knowledge graphs (VKGs), which allow organizations to query and analyze data from disparate sources without the need for data migration. In this article, we will explore what a VKG is, how it works, and its potential business applications.
 6 min

A Quickstart Guide to Ontopic Studio

This guide is for people who want to create a knowledge graph containing facts and KPIs from a database. Learn in 15 minutes how to create a virtual knowledge graph (VKG) without writing code from almost any database.
 15 min

Map column values to classes in your knowledge graph without the usual pain

The traditional way of mapping column values to classes when building a knowledge graph is annoying. You have to write many SQL queries or break the clear separation between data from the source and the ontology. In this article, we show you how to map a class based on the value in a given column in a single step with Ontopic Studio.
 5 min

Mapping many-to-many relationships to object properties

Connecting things is undoubtedly the characteristic feature of a knowledge graph, and this pattern helps you to build highly connected knowledge graphs from database tables.
 5 min

Mapping database columns to data properties

Mapping table columns to data properties (attributes) in the knowledge graph is another fundamental pattern you will use frequently.
 5 min

Mapping a database table to a class

The pattern presented in this article will likely be your first mapping pattern when building a knowledge graph from a relational data source.
 5 min

Some facts to consider when choosing a Mapping Editor

There is more than one way to map data sources to knowledge graphs. We shortly describe the most common ones and advice how to choose which way to go.
 7 min

Why using mappings for building a knowledge graph

Mapping data sources to the integrated data solution is essential in many data integration projects. We started a series on mapping data with this first article.
 3 min