Since Google announced its Knowledge Graph solution in 2012 the paradigm has found its way into many real-world use cases. These are mostly in the analytics space. The graph database market has exploded over the last 10 years with at least 50 brand names today. International Standardization is coming – very soon SQL will be extended by functionality for property query queries. A full international standard for property graphs, called GQL, will surface in late 2023.
The inclusion of graph technology dramatically enlarges the scope of analytics by enabling semi-structured information, semantic sources such as ontologies and taxonomies, social networks as well as schema-less sources of data. At the same time graph databases are much better suited for doing complex multi-joins analyzing large networks of data, opening up for advanced fraud detection etc. The Panama papers is the best-known example. Finally graph theory is a mathematical discipline with a long history, which among other things have created graph algorithms for many complex analytics, such as clustering, shortest path, page rank, centrality and much more.
This presentation will cover what a Knowledge Graph is, how it is different and yet complementary to other technologies. Furthermore, Thomas will cover:
- Why do semantics and relations matter?
- What kinds of data architectures and pipelines?
- Which are the vendors and the products?
- Which standards exist?
It is a non-technical presentation, focusing on business requirements and architecture. More technical information will be covered in the workshop Understanding Graph Technologies on the 5th of April.