Keynote

Semantics has unique benefits for content. Whether polished marketing material or accurate, insightful employee enablement, all audiences gain by quickly finding what they want. Beyond findability, recommendations take individual users to their goals with a minimum of personal data.

Knowledge graphs (KGs) have momentum as expressive data structures to represent the convergence of data and knowledge spread across various data sources. Albeit coined by the research community for several decades, KGs play an increasingly relevant role in scientific and industrial areas.

In challenging times, ensuring financial integrity and fairer distribution of services by reducing disparities are among the top priorities for social and health-care systems globally.

Robots operating in the real world require sophisticated Visual Intelligence (VI), to be able to make sense of the variety of situations they may encounter. Computationally, the basic capability here has to do with Object Recognition – as a minimum, a visually intelligent robot needs to be able to recognise the content of its observations.

In this presentation, I will show through a number of examples how Linked Open Data, and especially DBpedia, have contributed to AI by making it possible to create intelligent, open domain applications, i.e. applications which do not have a fixed domain, or for which this domain is not known in advance. This was made evident through a number of high profile applications (e.g.

“Code is Law” – three famous words of Professor Lawrence Lessig back in 1999, when the Internet as the first important “cyberspace” emerged. This raised fundamental questions about how Code will impact our legal environment. Since then IT has further moved into our lives and now eventually reaches out to the legal profession. Major questions raised since then are still valid.

How one can link structured to unstructured data to get a holistic view and generate more insights.