In this blog post, we’ll explore how to build a massive knowledge graph from existing information or external sources in a repeatable and scalable manner. We’ll go through the process step-by-step, and discuss how the Graph-Massivizer project supports the development of multiple large knowledge graphs and the considerations you need to take when creating your own graph. Keep reading!

This post has also been published on the Digital Science TL;DR website. 

Despite the increasing availability of public data, why are so many pharma and life sciences organizations still grappling with a persistent knowledge divide? This discrepancy was a focal point at the recent BioTechX conference in October, Europe's largest biotechnology congress that brings together researchers and leaders in pharma, academia and business. In this post, we explore the need to connect data from different sources and all internal corporate data through one, integrated semantic data layer.

At metaphacts we help customers leverage knowledge graphs to unlock the value of their data assets and drive digital transformation. We started out with this mission in 2014 and, since then, we've served a multitude of customers in pharma and life sciences, engineering and manufacturing, finance and insurance, as well as digital humanities and cultural heritage.

This blog post will give you an overview of what we have developed in customer projects over the years as our game plan to build a Knowledge Graph-driven, FAIR Data platform and drive digital transformation with data. The post will show you how our product metaphactory can support you every step of the way, and will highlight examples from the life sciences and pharma domains.