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Dr. Geoffrey P Malafsky TECHi2

A New Technology for Unifying Knowledge and Semantics to Harness the Fusion of People-Process-Technology. Dr. Geoffrey P Malafsky TECHi2. The Problem Space. Information overload Effective time compression Confidence in decision making and actions Cost increases Budget decreases

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Dr. Geoffrey P Malafsky TECHi2

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  1. A New Technology for Unifying Knowledge and Semantics to Harness the Fusion of People-Process-Technology Dr. Geoffrey P Malafsky TECHi2

  2. The Problem Space • Information overload • Effective time compression • Confidence in decision making and actions • Cost increases • Budget decreases • Ramifications of error Capitol Science Forum 29 March 2008

  3. Holy Grail (or Faustian Bargain ?): Intelligent Automation From James Hendler, Agents and the Semantic Web, IEEE Intel Sys, Mar/Apr 2001 Capitol Science Forum 29 March 2008

  4. Aspects of Knowledge Discovery Status Complexity Knowledge Representation Data Volume Human-Computer Interaction Complex Relational Information Relations across time and space for people, places & things Vast >106 attributes, links, nodes Iterative IncrementalActive Learning Unspecified, evolving problem Far Beyond State-of-Art Advanced Discovery Simple Relational Information Relations among people, places & things Substantial 103 - 104 attributes, links, nodes Interactive User-specified problem, with suggested retargeting Some prior knowledge Guided Discovery Beyond State-of-Art Propositional Information Simple attributes for people, places & things Minimal 100s of attributes, links, nodes Negligible User-specified problem No prior knowledge Naïve Discovery State-of-Art Technology Status Capitol Science Forum 29 March 2008

  5. Melding People-Process-Technology into More than a Maxim • Engineering a solution through analysis, trade-off, and integration • Explicit definition of human meaning mapped to machine functions • Continuous, iterative improvements • The glue: A streamlined framework mapping from human knowledge to explicit concepts to rules to semantics Capitol Science Forum 29 March 2008

  6. The Enterprise Data Environment (EDE) Arises From Lessons Learned • Industry recognition of failure of technology and business processes to both meet current and future information challenges and the need for a “technology” that can be used in real organizations • Standards for metadata, object models, data storage, data transmission, coding • Acceptance of need for unified designs coupling people (aka organizations) to technology (aka computer tools) to process (aka specified procedures) Capitol Science Forum 29 March 2008

  7. Command and Control Foundation • Worked on technology development to meet DoD’s plan for Net-Centric operations for past 10 years Net-Centric Warfare JOINT VISION 2020 Capitol Science Forum 29 March 2008

  8. National Intelligence Foundation • EDE part of technology for large volume, distributed, disparate, data assets for Geospatial Intelligence Data driven semantics for authoritative, meaningful analysis Capitol Science Forum 29 March 2008

  9. Phasic Systems Inc’s Semantic Technology: KORS Capitol Science Forum 29 March 2008

  10. Each sub-ontology is decomposed into greater detail used as classes in system* *Patent-pending Capitol Science Forum 29 March 2008

  11. Knowledge Discovery with KORS Capitol Science Forum 29 March 2008

  12. Capitol Science Forum 29 March 2008

  13. Data Models Flexible, attribute-based directly ties governance rules to execution in database with simple XML configuration Capitol Science Forum 29 March 2008

  14. A Brief Demonstration

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