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DESCRIPTION:Click for Latest Location Information: http://edw2022.dataversi
 ty.net/sessionPop.cfm?confid=129&proposalid=13514\nMany organizations have 
 discovered that using machine learning, natural language processing (NLP), 
 and other graph-based analyses to address real-world business challenges ca
 n be costly, and does not always result in anticipated gains in efficiency 
 or insight. A significant portion of the budget is required to develop trai
 ning sets, which are not always reusable. It can also be difficult to under
 stand the results.\nRecent developments in standards that bridge the proper
 ty graph / knowledge graph divide make it much easier to use more expressiv
 e knowledge graphs as the basis for these kinds of analytics. A number of w
 ell-known graph database vendors have implemented (or are implementing) the
  emerging RDF* / SPARQL* W3C recommendation to allow users to use RDF vocab
 ularies and OWL ontologies as the basis for machine learning, NLP, and othe
 r analytics. Using richer vocabularies improves the results obtained from v
 arious analytical tools and can support explanation generation in ways that
  property graphs alone cannot.\nIn this one-day course, we will provide an 
 overview of the ontology engineering skills needed to create richer models,
  particularly those geared towards machine learning and NLP applications, a
 nd share lessons learned to help users get started. \nAttendees will l
 earn tips and best practices for:\n\n
 Ontology basics – definitions, underlying logic fundamentals\n
 Business requirements and use-case-driven approach\n
 Development methodology with a focus on terminology work and conceptual mod
 eling\n	Common, reusable modeling patterns with hands-on exercises\n
 Guidelines for achieving more consistent results, including naming conventi
 ons, rules of thumb\n
 Evaluating and reusing existing ontologies and data sets\n
 Querying and using ontologies in knowledge graph-based applications\n
 Real-world examples of successful implementations\n\n
DTSTART:20220324T134500
SUMMARY:Ontology Engineering for Knowledge Graphs
DTEND:20220324T165959
LOCATION: See Description
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