The relational databas e has the limitation of expressing human knowledge because it cannot incorp orate semantics into the data model. Semantic technology is a new way of st oring information, which combines data representation and human knowledge i nto a machine-readable format. This can be achieved by RDF/OWL and triple s tore technologies. From our previous Proof of Concept presentations in this forum, we showed exploratory Enterprise Knowledge Graph (EKG) transforming conventional Relational Datastore into triples using FIBO, an industry sta ndard ontology expressed in RDF/OWL. EKG is built on top of two pillars of knowledge base: the data from legacy systems and human knowledge embedded i n (extended) FIBO. It enables "Knowledge Analytics" utilizing the power of “Logical Inference.”
\nThe rise of “Machi ne Intelligence” is adding even more opportunities to Knowledge Analy tics through probabilistic inferences. In this presentation, we will introd uce machine learning to 'augment’ the knowledge base. New insight s discovered by machine learning models can be added back to EKG as new kno wledge. Logical Inference through RDF/OWL technologies, together with Proba bilistic Inference through machine learning, create a powerful combination to extend the knowledge analytics to much higher potential to solve real-wo rld financial industry problems.
\nThe presentation includes se lected use cases for common data issues in financial services firms such as matching master data from different sources, identifying unknown entities and classification of entities, and demonstration of the solution using gra ph DB technologies and R.
\n DTSTART:20180424T133000 SUMMARY:LI*MI: Knowledge Analytics with Augmented Intelligence DTEND:20180424T142959 LOCATION: See Description END:VEVENT END:VCALENDAR