Ruben Verborgh is a researcher in semantic hypermedia at Ghent University – iMinds, Belgium, where he obtained his PhD in Computer Science in 2014. He explores the connection between Semantic Web technologies and the Web's architectural properties, with the ultimate goal of building more intelligent clients. Along the way, he became fascinated by Linked Data, REST/hypermedia, Web APIs, and related technologies. He's a co-author of two books on Linked Data, and has written more than 100 publications on Web-related topics for international conferences and journals.
1 - #Semanticsconf: What's your personal story with semantic technologies?
The famous Semantic Web article quickened my imagination many years ago: were intelligent agents really going to do things for us on the Web? You can imagine my disappointment when I started to attend Semantic Web conferences, where people mostly talked about servers rather than clients. Fortunately, I slowly see things changing, as developers are gradually paving a pragmatic path towards a Linked Data ecosystem. Our research group within iMinds aims to be part of this effort by researching various ways to enable intelligent clients. In the future, I hope to see a stronger community focus on clients and applications, bringing us closer to the initial Semantic Web vision.
2 - #Semanticsconf: What do you consider the most promising project with respect to Semantic Technologies and Industry?
I'm quite happy to see what's happening in the Hydra W3C Community group, where a realistic link with real-world Web APIs is under development. In contrast to the more theoretical and RDF-centric work on services, these people focus on building applications that work.
3 - #Semanticsconf: Did you experience a remarkable moment with semantic technologies/artificial intelligence?
Even though the story I'd like to share didn't have a happy ending, it definitely was amazing: Sindice and Sig.ma. With Sig.ma, you could enter any topic, and you would gradually receive more and more related Linked Data facts from all over the Web. The amount of information it brought revealed the huge potential of Linked Data, beyond what search engines bring today. The centralized approach with the Sindice index, however, was probably not the right one. Yet this example can be an inspiration for the years to come: can we build a semantic search engine for end users in a decentralized way?
+1 - #Semanticsconf: What do you consider the most inspiring read / tool / project on Semantic Technologies?
The work on the Linked Data processor SQUIN really inspired me. Rather than assuming that all data is in a single place, Linked Data query execution traverses the Web for more information—just like we do as humans. I really believe the Semantic Web community needs more "Web", and initiatives like this get the ball rolling.