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Ruben Verborgh

Ghent University
Ruben Verborgh is a professor of Decentralized Web technology at IDLab, Ghent University – imec, and a research affiliate at the Decentralized Information Group of CSAIL at MIT. Additionally, he acts as a technology advocate for Inrupt and the Solid ecosystem wherein people and organizations control their own data. He aims to build a more intelligent generation of clients for a decentralized Web at the intersection of Linked Data and hypermedia-driven Web APIs.

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In today’s data-driven economy, the company with the most data wins. This means that companies go through great lengths to collect all of our data, sometimes even crossing ethical boundaries. But in the end, we all lose: having the best data process is not an indication of how well a company innovates or what value its services bring. As a result, innovation has come to a standstill, and people are stuck with mediocre data experiences. If data truly is the new oil, then what we’re currently doing is pouring all that oil in barrels—often without the ability to open the lid ourselves. In order for data to fuel the engine, it has to flow much better than it does today. In this talk, I will explain how the Solid project gives people back control over their personal knowledge graphs. In doing so, we enable small and large companies alike to innovate.

May 7th 2020, 11:40pm EST

Q&A Section with Ruben Verborgh from Ghent University, Byron Jacob from data.world and Yanko Ivanov from Enterprise Knowledge.

May 5th 2020, 12:30pm EST

Electronic health records (EHRs) have become a popular source of observational health data for learning insights that could inform the treatment of acute medical conditions. Their utility for learning insights for informing preventive care and management of chronic conditions however, has remained limited. For this reason, the addition of social determinants of health (SDoH) [1] and ‘observations of daily living’ (ODL) [2] to the EHR have been proposed. This combination of medical, social, behavioral and lifestyle information about the patient is essential for allowing medical events to be understood in the context of one’s life and conversely, allowing lifestyle choices to be considered jointly with one’s medical context; it would be generated by both patients and their providers and potentially useful to both for decision-making. We propose that the personal health knowledge graph is a semantic representation of a patient’s combined medical records, SDoH and ODLs. While there are some initial efforts to clarify what personal knowledge graphs are [3] and how they may be made specific for health [4, 5], there is still much to be determined with respect to how to operationalize and apply such a knowledge graph in life and in clinical practice. There are challenges in collecting, managing, integrating, and analyzing the data required to populate the knowledge graph, and subsequently in maintaining, reasoning over, and sharing aspects of the knowledge graph. Importantly, we recognize that it would not be fruitful to design a universal personal health knowledge graph, but rather, to be use-case driven. In this workshop, we aim to gather health practitioners, health informaticists, knowledge engineers, and computer scientists working on defining, building, consuming, and integrating personal health knowledge graphs to discuss the challenges and opportunities in this nascent space.