Why Semantic Technology does not have wider acceptance

Semantic Technologies and

Big Business (and Small)

SemTechBiz 19-20 September 2012

Antonia Bradford

Copyright AB Computing Limited 2012 1

Two hats


Today’s thoughts


Brilliant Ideas?

Today’s thoughts


Logic

Today’s thoughts


TALK Business Machinery

We won’t discuss


RDF

SPARQL

N-Triples Inference

Graphs

URIs

Tags

OWL

Instead


Investment Traceability

Fraud Prevention

Crisis Response

Profit

Risk Reduction

Scientific collaboration

Cost - Benefit

Bottom line

Market Share

Business Case


Topic

How can the customer make more

MONEY and

Solving Problems


£n00.000.000.000 £n00.000

All sizes


£n00.000.000.000


Understanding

• What is ‘Semantic Technology’?

No…lots of misconceptions

At best: Semantic searches

At worst: Sharepoint


Why?

–Not ‘core’

–Not Strategic

–No push from mainstream suppliers

–NO CLEAR BUSINESS CASE

–NO CLEAR MESSAGE

Boxed in


Reasons


Inertia Vested Interests

No imagination


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


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

Negative cycle


Level of Risk

Development Resources

Understanding of Capabilities

Level of Demand

Must be broken


Level of Risk

Development Resources

Understanding of Capabilities Level of

Demand

MUST


Solution Architects, Bid writers

Toolset that is REALLY EASY TO

USE

Break the cycle


Positive Cycle


1 Development

Resources

2

Increased Understanding of Capabilities

3

Perception of Lower Risk

4

Increased Level of Demand


Excellence

–Fluid data models

–Very rapid development

–Relationships as important as the data

–Heterogeneous objects


Business Case

• Improve some existing tasks

• Answers questions Relational model can not answer

• Opens new business opportunities


Articulate advantages

•Outcomes •Results •Cost reduction •Expansion •Discovery


Don’t

•Semantic •Inference •Triples •SPARQL


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


Relational vs Semantic

Washing Machine? Or Dishwasher?

BOTH!

Don’t get boxed


NOT JUST Semantic Web Semantic Technology is …


‘NO’ to Dataset Discrimination

‘YES’ to

Open Closed

Big Small

Stand Alone Networked

All types of Federation Hard

Soft

Homogeneous

Heterogeneous

Challenge


The Art of the Possible Rigour

+ =

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


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…


Applications

• Traceability

– Materials

– Food

– Animal genetics

– Animal movements

– Migration of wild animals

– Use of chemical compounds in medicines


Applications

Epidemiology

• Biological

– Animal

– Human

– Plant

• Technological (Computer virus)

• Financial (Toxic Assets)


Applications

• Brain Storming

•Mind mapping that goes beyond pictures

–Represents real knowledge

–Can be queried

•Collaborative research (already starting)


Applications


Fraud Prevention

Criminal Investigations

Intelligent Alerts

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

Out of the lab


Scalable


Performant


Secure


End to End


Start Middle End

Sharing


PRIVATE CLUB

Single Organisation


S

Y

S

1

S

Y

S

2

S

Y

S

3

S

Y

S

4

S

Y

S

5

Shared information

Across Organisations


I

C

1

I

C

2

I

C

3

I

C

4

I

C

5

Shared Claim information


We

are businesses too!

MONEY


Danger points

Running out of money

Running out of faith

• 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.

SUMMARY


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