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Using TypeDB for Autonomous Vehicles

The team at TNO set out to combine knowledge of human driving — e.g. ethics, laws, norms, preferences, common environmental understanding — with the technological side of autonomous driving — e.g. control theory, data-driven AI, black-box algorithms (DNN), and end-to-end learning. To achieve this and be able to reason over the collective knowledge, they used TypeDB.

An example where the combination of this knowledge demonstrates its value is when hardcoded knowledge meets contextual knowledge of a given situation in time. Let’s say an autonomous vehicle is programmed to drive slower in school zones, obviously needed for safety, but what about when the school is closed? Does the vehicle still need to drive at a slower speed?

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Migrating a Spacecraft Engineering Model in UML to a Knowledge Graph

Goal: Migrate the UML-based engineering model of a spacecraft to TypeQL

Why Do This Migration in the First Place?

The spacecraft lifecycle is roughly divided into seven consecutive design phases. Part of the early design phases deals with the feasibility of the intended mission. Feasibility is identified by assessing each design aspect that is needed to accomplish the specific mission.

This requires that engineers lay out all possible design options and iteratively go through them in relation to all the other engineering design options, ultimately ending up with a sound system solution.

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Modeling and Loading Data at Scale

Back in April we hosted an online conference for our community, Orbit 2021, and in listening to Henning Kuich, Dan Plischke, and Joren Retel from Bayer Pharmaceuticals, the community got a glimpse into how a team within Bayer Pharmaceuticals uses TypeDB to accelerate their drug discovery pipelines.

Objective

The team at Bayer fundamentally wanted to understand diseases better so that they can create better therapeutic interventions.  A deeper understanding of diseases enables the identification and development of novel therapeutic interventions that have little to no side effects.

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An Enterprise Data Stack Using TypeDB

At Bayer, one of the largest pharmaceutical companies in the world, gaining a deep understanding of biological systems is paramount for the discovery of new therapeutics. This is inspiring the adoption of technologies that can accelerate and automate discovery, spanning all of the components of their data infrastructure, starting with the database.

The challenges posed within the data and discovery process are not unique to Bayer:

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Building a Biomedical Knowledge Graph

Debrief from a Vaticle Community talk — featuring Konrad Myśliwiec, Scientist, Systems Biology, at Roche. This talk was delivered virtually at Orbit 2021 in April.

Konrad, like so many TypeDB community members, comes from a diverse engineering background. Knowledge graphs have been part of his scope since working on an enterprise knowledge graph for GSK. He’s been a part of the TypeDB community for roughly 3 years. While most of his career has been spent in the biomedical industry, he’s spent time working on business intelligence applications, developing mobile apps and currently as a data science engineer in the RGITSC (Roche Global IT Solutions Centre) for Roche Pharmaceuticals.

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Social Graphs for Drug Development

Back in February, when we could all gather safely still, Grakn Cosmos, Grakn Labs' first global user conference, hit London; and Paul Agapow, Health Informatics Director at AstraZeneca, spoke about his team's work in building a social graph to reduce time and financial resources when recruiting for clinical trials.

…this is a first step in it, for us to develop expertise to explore, to see where we can go - we are people with problems to solve.

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A Clinical Decision Support System Built With a Knowledge Graph

Debrief from a Grakn Community talk — featuring Alessia Basadonne, executive PHD candidate from University of Pavia and Medas Italy. This talk was delivered live at Grakn Cosmos 2020 in London, UK.

“From when I was very very little, I always dreamed of developing crazy ideas and making them a reality.”

Alessia’s current work is in developing a Clinical Decision Support System (CDSS). This isn’t a new concept as she highlights, but one with a lot of opportunity for improvements and developments. So…