Musings on why Semantic Technology has not been taken up by large and small businesses. I look at the issue from the point of view of the CONSUMER, hence the Bowler hat, and the PRODUCER, the mention to the two products that AB COMPUTING is working on.
11. Understanding
• What is ‘Semantic Technology’?
No…lots of misconceptions
At best: Semantic searches
At worst: Sharepoint
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12. Why?
–Not ‘core’
–Not Strategic
–No push from mainstream
suppliers
–NO CLEAR BUSINESS CASE
–NO CLEAR MESSAGE
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14. Reasons
No imagination
Inertia Vested Interests
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15. Relational
(Consumer View)
• Widespread adoption:
– Ease of use
– Accessible at different levels
• Satisfies most business requirements
• Established supplier chain
• Low risk:
– Easy to model, accessible, understood
– Easy to find resources
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16. On the offensive
• Need to increase the adoption
• Need to focus on what it excels at
• Must make sure we stay in business, need to
maintain healthy competition (!)
• Need to reduce the risk of introducing a new
Technology
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17. Negative cycle
Level of Risk
Level of Development
Demand Resources
Understanding
of Capabilities
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18. Must be broken
Development
Resources
Level of Risk
Understanding
Level of of Capabilities
Demand
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19. MUST
Solution Architects, Toolset that is
Bid writers REALLY EASY TO
USE
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21. Positive Cycle
1
Development
Resources
4 2
Increased Increased
Level of Understanding
Demand of Capabilities
3
Perception of
Lower Risk
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22. Excellence
–Fluid data models
–Very rapid development
–Relationships as
important as the data
–Heterogeneous objects
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23. Business Case
• Improve some existing tasks
• Answers questions Relational
model can not answer
• Opens new business opportunities
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26. Businesses expect
–Easy tools for Data Manipulation
–Clear discipline in design and data entry
–Easy integration with existing systems
–Rigorous security model
–Reliable and resilient
–Rich reporting and printing capabilities
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27. Relational vs Semantic
BOTH!
Washing Machine? Or Dishwasher?
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28. Don’t get boxed
Semantic Technology is … NOT JUST Semantic Web
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29. ‘NO’ to
Dataset Discrimination
‘YES’ to
Open Closed
Big Small
Stand Alone Networked
All types of Federation Hard
Soft
Homogeneous
Heterogeneous
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30. Challenge
+ =
The Art of the Possible Rigour
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31. Applications
• Content Management and reuse
– Television ( as the BBC have done)
– Syndication of printed content for newspapers and
magazines (audio, textual and graphical.)
• Catalogue content
– Libraries
– Museums
– Online shopping
– Job listings
– Parts lists and Components
– Online Directories
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32. Applications
• Network representations
– Computer
– Rail, Road, Air
• Tourism information
• Constituent parts of complex objects
– Engineering: shopping trolleys to space rockets
– Architecture: Flat-pack houses to skyscrapers
– Furniture, Spectacles, Clothing…
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33. Applications
• Traceability
– Materials
– Food
– Animal genetics
– Animal movements
– Migration of wild animals
– Use of chemical compounds in medicines
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37. Smart Nation:
Single view of the citizen
Local Government Police sourced Data
Fiscal data (tax) Traffic Data
Social Security Data Judicial Data
Health Data Education
Border Control Forensic Analysis
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38. Out of the lab
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48. SUMMARY
• Dream new applications
• Share Ideas
• Develop tools that are accessible
• Build alliances: Associate and cooperate with
other suppliers
• Hold a tight rein on the finances
• Consolidate the marketing
• Pace yourselves; plan to stay in for the long run.
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49. Meet
AB’s Wolf pack
1st October
EQL 0.1
Beta EQL
Web version
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