Seven Arguments for Semantic Technologies

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This presentation lays out the seven main arguments for why it makes sense to be interested in semantic technologies for the enterprise. It is provided by Mike Bergman, noted observer of the semantic technology scene and CEO of Structured Dynamics LLC.

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Seven Arguments for Semantic Technologies

  1. 1. Seven Arguments forSemantic Technologies Michael K. Bergman February 24, 2013
  2. 2. Summary Points© Copyright 2013. Structured Dynamics LLC 2
  3. 3. Knowledge-based Findability  A “unifier” of search:  structured + semi-structured + unstructured data  find + discover + navigate  SQL + SPARQL + full-text  “dial-in” search results  Global faceting:  types of things  relationships of thing  Relationships bring context  Explodes keyword boundaries:  concept enrichment  meaning and many ways to describe things  multi-linguality  inference© Copyright 2013. Structured Dynamics LLC 3
  4. 4. Ability to Do More – Classic IA© Copyright 2013. Structured Dynamics LLC 4
  5. 5. Going Beyond Content and Context  Convertibility  single data representation model  the “universal solvent”  Data interoperability  Semantic annotation and tagging:  concepts – OBIE  entities – NER  semantic injection  Understood equally by machines + humans  Computability  Analysis  Superior performance  Semantic publishing© Copyright 2013. Structured Dynamics LLC 5
  6. 6. The Knowledge Graph  Built from simple (“triple”) assertions:  easily understood  easily corrected  concepts and many ways to speak of them  can capture rich relationships  rich ways to organize  Based on logic:  may be reasoned over  testable for coherence  Supports unique capabilities:  graph traversal  graph analysis© Copyright 2013. Structured Dynamics LLC 6
  7. 7. Control Shifts to the Business  Major driver is the ontology, structure of information  Content is separated:  from presentation  from applications  SMEs and knowledge workers now:  model the business, workflows  extend structure  expand concepts, meaning  author content, manage datasets  IT now shifts to:  developing generic applications (“ODapps”)  stack deployment and maintenance  security, content versioning and backups© Copyright 2013. Structured Dynamics LLC 7
  8. 8. Adaptive, Robust Structures  The right data representation model:  RDF v RDBMs  open world v closed world assumption  representation = knowledge + access  Integrating heterogeneous information:  syntactic  structure (“granularity mismatch”)  semantic  More effective HCI (human-computer interface):  separation of content and presentation  templates  ontology-driven (generic) applications© Copyright 2013. Structured Dynamics LLC 8
  9. 9. Much Reduced Costs  Leverage existing information assets  Single representation of schema  Use of generic, ontology-driven tools  Extendable without re-architecting  Reduced errors  Higher quality information in context  Lower deployment and set-up costs +  Lower maintenance and extension costs  average 1 – 2 orders of magnitude less© Copyright 2013. Structured Dynamics LLC 9
  10. 10. Domain Rationale (biomedical example)  Veritable explosion in data (“Big Data”)  Veritable explosion in new concepts, new knowledge  Rich, layered, complex relationships:  many disciplines and sub-disciplines  many informational perspectives  many players  much varied terminology  fit with electronic health records (EHR)  Strong, existing support for semantic technologies:  more than 250 extant ontologies, ontology groups  the OBO Foundry  the National Center for Biomedical Ontology  major emphasis of US National Institutes of Health and its National Library of Medicine© Copyright 2013. Structured Dynamics LLC 10

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