Lotico oct 2010

Introduction to Semantic Web
Dean Allemang
Chief Scientist, TopQuadrant Inc.
dallemang@topquadrant.com

© Copyright 2007-2010 TopQuadrant Inc. Slide 2
 Formed in 2001
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Corporate Overview

Semantic Representation of Data and Models
(Ontologies)
A model of the concepts and relationships between concepts within a specific domain.
World Wide Web Consortium (W3C) Semantic Web Standards:
- RDF - RDFS
- SPARQL - OWL

Semantic Web: Make web content machine-readable!
“The Semantic Web is a vision: the idea of having data on the Web defined
and linked in a way that it can be used by machines not just for display
purposes, but for automation, integration and reuse of data across various
applications.[W3C 2001] ”
“The Semantic Web is an extension of the current Web in which information is
given well-defined meaning, better enabling computers and people to work in
cooperation.” [Tim Berners-Lee et al 2001]

What could the Web do?
Web page interaction –
uses people as its medium!

What could the Web do? (cont.)
Can this sort of
interaction
become part of
the Web itself?

How could the Web do it?
Built-in by the Webmaster
Agree upon an “interlingua”

A new Web of terminology
Use the same technology for mapping web pages
to terminology
to map terminology to one another
What’s the Interlingua for the Interlingua?

What about people? Don’t they make the world go
round?
Ontology
Ontology
Annotated
Web Page
Annotated
Web Page
computers and people…better cooperation
Annotated
Web Page
Agent
Ontology
Human
Internet
Source:, Phil Windridge WSWS (2004)

The Web: The World’s Largest
Information System!
How did it get so big? What is special about The Web?

Semantic Web Standards Stack

How Semantic Languages Work
 Bring information together
 Draw inferences
RDF
RDFS
OWL

What is RDF? Distribution of data
ID
Model
No.
Division Product Line
Manufacture
location
SKU
In
Stock
1 ZX-3
Manufacturing
support
Paper machine Sacramento FB3524 23
2 ZX-3P
Manufacturing
support
Paper machine Sacramento KD5243 4
3 ZX-3S
Manufacturing
support
Paper machine Sacramento IL4028 34
4 B-1430
Control
Engineering
Feedback Line Elizabeth KS4520 23
5 B-1430X
Control
Engineering
Feedback Line Elizabeth CL5934 14
6 B-1431
Control
Engineering
Active Sensor Seoul KK3945 0
7 DBB-12 Accessories Monitor Hong Kong ND5520 100
8 SP-1234 Safety Safety Valve Cleveland HI4554 4
9 SPX-1234 Safety Safety Valve Cleveland OP5333 14

Distribute by rows?
1 ZX-3 Manufacturing support Paper machine Sacramento FB3524 23
4
B-
1430
Control
Engineering
Feedback
Line
Elizabeth KS4520 23
7 DBB-12 Accessories Monitor Hong Kong ND5520 100
Needs common schema
- which column is which?

Distribute by columns?
Division
Manufacturing
support
Manufacturing
support
Manufacturing
support
Control
Engineering
Control
Engineering
Control
Engineering
Accessories
Safety
Safety
In Stock
23
4
34
23
14
0
100
4
14
Model No.
ZX-3
ZX-3P
ZX-3S
B-1430
B-1430X
B-1431
DBB-12
SP-1234
SPX-1234
Needs to reference
entities – which thing are
we talking about?

Distribute by cells!?
Needs to reference both
schema and entities
Most flexible – can distribute
data in any way at all!
Division
7 Accessories
Product Line
4 Feedback Line
Model
1 ZX-3
Division
7 Accessories

Distribute by cells!?
Division
7 Accessories
Subject
Predicate
Object
URI’s
Triple Store
•Store,
•Index, and
•Federate
these triples

Thinking “Outside of the Table”
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
01-02 9.3 6.4 3.5
3.0 2.8
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production-
crops4 01 7.6 5.3
3.0 2.4 2.3
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
production -
livestock4 02 1.7
1.1 0.5 0.6 0.6
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
Agricultural
services 07 12.4
7.6 4.4 3.2 4.8
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
1.4 0.8 0.4 0.4
0.6
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Oil and gas
extraction 13 1.0
0.5 0.2 0.3 0.5
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2 0.1
0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
Nonmetallic
minerals mining6
14 0.4 0.3 0.2
0.1 0.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
49.8 32.7 23.5
9.3 17.1
Adapted from a slide by Dean Allemang
From Tables to
Linked Data

Representing Data in Graphs
Graph = nodes linked by labeled edges

What is RDFS?
RDFS is the schema language for RDF
Type inferences can be made, based on schema

Why is RDFS useful?
 RDFS allows us to talk about classes of instances
 It provides inferences, e.g.,
 Best Western is a Hotel
 (and hence, anything we know about Hotels
applies to Best Western)
 RDFS is in RDF (it’s its own schema language!)

gov: EPA
A little RDF(S) goes a long way
gov: department
gov: agency
gov: body
A model of government agencies and
departments. Such models are called
Ontologies.
brm: Business Area brm: Line Of
Business
brm:
subfunction
brm:Resource
Mgmt
brm: s2citizens brm:Energy
eGOV: capability
eGOV:
Standard
eGOV:Service Spec
eGOV: Remote
Reporting
eGOV: web
service
eGovOS: project
gov: FERCgov: DoE

Ontologies are the means to separate “what is
common” from “what is different”
From Tim Berners-Lee, ISWC 2003
Semantic map:
Connecting silo
domains

OWL (formerly DAML+OIL)
What is OWL?
 The “Web Ontology Language”
 W3C Standard DAML
DAML+OIL
OIL
OWL
RDF
DARPA EU (various)
W3C
Became a
Recommendation in
February 2004
... for Owl, wise though he was
in many ways, able to read and
write and spell his own name ...

Function Start Date OperatedBy
Air And
Radiation
Jan 1, 1994 EPA
Financial
Management
Aug 31, 1999 OMB
Compensation Oct 1, 2003 OMB
… … …
<Agency name=“EPA”>
<OperatesLOB>SWER</OperatesLOB>
<OperatesLOB>Water Quality Reporting</OperatesLOB>
<OperatesLOB>Acid Rain Monitoring</OperatesLOB>
</Agency>

OWL can specify rich relationships: equivalence,
inverse, unique, …
Q: What is operatedBy EPA?
A: AirAndRadiation
A: SWER, AirAndRatiation
A: SWER

SPARQL
SPARQL Protocol and RDF Query Language
Query Language (like SQL is for databases, XQuery
is for XML, etc.)
Extracts information from a graph using pattern
matching
“Four-star lodging in New York”

Find data in a graph

TopBraid Suite™
Enterprise Application
Deployment and Use
(Ontology Modeling and
Application Development
to
Complete Semantic Application Lifecycle Support
Semantic Web Modeling and
Application Development
Environment
Enterprise Platform
for Semantic Web
Applications
Semantic Web
Application Assembly
Toolkit

Data.Gov
 Data.gov – collection of government data sets made
available to the world to use

Sample Dataset: #1329

FHEO Filed Cases
 What does the data look like?

Metadata for the data
 What are the fields? What do they mean?

How can we use it?
Dataset 1329
FHEO Filings
(data.gov)
Counties/
Locations
SPARQL Rules
FHEO Data Description
TopBriad Suite
uses Semantic
Web Standards
to mash up data

Display data mash-up
 Rules for color coding encoded in SPARQL Rules
 Yahoo! Map served up through TopBraid Ensemble™

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