Semantic Web languages such as RDF, RDFS and OWL are suitable for representing static knowledge, which enables the integration of data and information from multiple systems. With knowledge graphs, these technologies are becoming widely deployed in enterprise settings. But many applications require more than just information integration.
The concept of Linked Open Data and the promise of the Web of Data have been around for over a decade now. Yet, the great potential of free access to a broad range of data that these technologies offer has not yet been fully exploited.
The Semantic Web is potentially the massive global open knowledge base that Artificial Intelligence has been looking for since its origins. After two decades, Linked Open Data is the closest existing exemplar of such a resource. Nevertheless, LOD and similar, maybe bigger, private knowledge graphs have unlocked just a little beyond encyclopaedic question-answering.
With its focus on improving the health and well being of people, biomedicine has always been a fertile, if not challenging domain for computational discovery science.
Michael J. Sullivan
With the growing need for volumes of data required by ML and Knowledge Bases, copying/duplicating potentially Petabytes of data is a real problem. Working with data "in situ" is fast becoming the only viable pattern for enterprises.
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.