Why I Ignored Ontologies and Graph DB and Why You Shouldn't
  Mark Ouska   Mark Ouska
Ontologist
Semantic Arts
 


 

Wednesday, November 15, 2017
08:30 AM - 09:20 AM

Level:  All - General Audience


Ontologies are models that capture meaning in a machine-readable form. If I told you my parents had three boys, Jim, Daniel, and me, you would know Jim and Daniel were my brothers, even though I didn’t tell you that. With an ontology, you can easily define family relationships so the computer can infer the unstated fact they are my brothers just like you, a human, can. A traditional data structure would require several joins. A graph database stores the schema and data together, pre-joined in packets called triples, i.e. George isParentOf Mark, Mary isParentOf Jim. The graph database can then be directly queried (with SPARQL) to retrieve the stated and infer the unstated data. But here’s the thing - the ontology creates the mathematical precision to know, not just guess at the unknown.

This IS the next disruptive technology, and you should invest the time to at least understand it!

  • Ontologies model meaning with mathematical precision.
  • Graph databases pre-join data under management.
  • Ontology-driven data management can accurately infer data that isn’t even there.
  • Data can be integrated in-place by its meaning rather than table and column names.
  • Come find out how ontology-driven systems will replace our current brittle environments.


Mark Ouska is an Enterprise Agile Data Strategist and Ontologist with nearly 30 years of professional information management experience in Enterprise Business Data Architecture. He's an Enterprise Data Strategy expert focused on data leadership and enterprise information management strategy development. He's demonstrated success recruiting key cross-functional business segments to participate in accurate technical articulation and execution of business data goals and objectives. He has proven expertise extracting business data requirements, developing relationships, and evolving models that execute the enterprise data vision. Public and private-sector leadership experience implementing solutions that are willingly adopted by technical staff, critical business leadership and championed by VP and C-Level constituents. Experience in multiple industries including Pharmaceutical Research, Retail, Health Care, Financial Services, Consulting, Criminal Justice, Insurance, Natural Resources, Petrochemicals, and Software Development.


 
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