Keynote

The human sense of smell is one of our most sensitive senses and a powerful warning system for potentially dangerous situations such as rotten food or a gas leak. However, detecting references to smell and representing these in a machine interpretable format is as yet an under-researched topic in computer vision, natural language processing and the semantic web.

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.