Industry

How can you find information in heterogeneous sources, available at different locations and managed by different owners? In the Dutch Digital Heritage Network (NDE) we are working on a distributed solution for this: the Network of Terms. We encourage our institutions to assign standardized terms to their digital heritage.

In this presentation we describe how we developed modules and pipelines for the industrial processing of data and content from a machine learning project in the legal domain. Lawyers’ work is far from being digitized. They need to deal with large amounts of printed documents of various types (e.g. letters, contracts, invoices, orders, offers, court documents).

Many industries and organisations face challenges with complexity. Many Firms in Financial Services are overwhelmed by complexity - complex, clients, products and markets, legacy technologies, acquisitions, regulations - and on top of it all, change as a constant.

In a drive to reduce the carbon footprint of their customers’ Grundfos is revolutionizing how they engage on efficiency savings of their cooling systems. Initial communication with the customers about cooling systems needs to capture complex system information, yet be simple and relevant for each of the many customer profiles to maximize retention.

In this presentation, we will show and provide technical details of Lynx, a Knowledge Graph and FAIR-fueled system to enable seamless access across the Roche semantic ecosystem. On the one hand, Lynx exploits machine-readable, FAIR Knowledge Graphs to allow for accessing and combining multiple and disparate reference data systems.

The volume of published, scientific data is growing at an exponential rate. When carefully curated and paired with emerging technologies such as knowledge graphs, the data is a powerful catalyst to accelerate innovation across a wide range of disciplines.

For the past decade or so, Knowledge Graphs have been sneaking into our daily lives, be it through voice assistants (such as Alexa, Siri or Google Assistant), intuitive search results or even personalized shopping experiences through online store recommenders.

With the growing need for volumes of data required by ML and Knowledge Bases, copying/duplicating potentially Petabytes of data is a real problem. Working with data "in situ" is fast becoming the only viable pattern for enterprises.

Making sense of data using semantics, open domain knowledge and standards to reach for interoperability within the public sector