Big Data has made many data processing tasks significantly easier over the past couple of years. We now have the capability to perform data processing like never before. However, Big Data comes with a Big Assumption: that we can bring lots of data together in one homogeneous dataset. But what if we can’t?
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.
Specifically trained bots - driven by Semantic Analytics and Artifical Intelligence - can identify substantial contradictions and other inconsistencies within tons of structured and unstructured data.
Javier D. Fernández
Ten years into Linked Data there are still many unresolved challenges towards arriving at a truly machine-readable and decentralized stage that would make the promised vision of a Web of Data come true. In this talk we will review the current state of affairs and highlight the key technical and non-technical challenges to the success of LOD.
How serious can a missing document on a search result list be? For certain people that conduct search as part of their profession, a missing search result could lead to litigation or death. This talk will examine search tasks in two such professional domains: Intellectual Property and Medicine, covering the challenges inherent in search in these domains.
How do you convince a well-run IT department to expand its horizon into the world of Semantics? How can you explain to business users the need to incorporate Semantics? What are the types of investments one has to make to start? What are the tools/techniques that best support Semantics and fit withing our organization?
Technology innovations aren't all created equal. Most are incremental improvements (Type I), layers of a new stack, for example, that accumulate over time. Stacks become towers of Babel, triggering struggles with complexity. Migrating to the cloud simply hands the complexity problem over to service providers.