Great article Ruben! I've been following Solid's progress for a while, and I think your article very eloquently summarize its purpose and relevance. I'm especia

Great article Ruben! I've been following Solid's progress for a while, and I think your article very eloquently summarize its purpose and relevance. I'm especially interested in the ability to circumvent the middle-men, and resolve the marketplace chicken-and-egg problem once and for all. Watching your TED talk in 2013 was one of the most influential moment in my life, and discovering the semantic web was perhaps my greatest epiphany. While the vision never left my mind, I never acted on it. Until now. I'm dedicating 2019 to linked data. I'm going all-in. Last week, I started to build a tool to convert unstructured input to linked data. Even after recognizing canonical literals (email, phone, url, color, gender, boolean, integer, float, date, time span, money, weight, distance, language, image, geo coordinates), I couldn't accurately infer predicates and guess classes. Before trying more complicated stuff like bayesian inference, I decided to try a simpler exercise. This time, I want to aggregate structured data from different sources and map it to some existing ontologies. For example, I want to convert some JSON about comments and links from Reddit and Hacker News to RDF using the http://schema.org http://schema.org vocabulary. - Can I feed the JSON into some ML system that automatically figures out the mapping? What if I provide some annotation or feedback? - Can I manually turn the JSON into JSON-LD and use that as the mapping information? What about complex transformations (different structures and literals)? - Should I implement the mapping manually using my favorite programming language? - Should I use R2RML or RML? What's the state of the art today for semantic data integration?