for Semantic Interoperability

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21 Οκτ 2013 (πριν από 3 χρόνια και 8 μήνες)

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Copyright 2004 by The MITRE Corporation

1

Dr. Leo Obrst

MITRE

Center for Innovative Computing & Informatics

Information Semantics

Lobrst@mitre.org

October 21, 2013


Ontologies & the Semantic Web
for Semantic Interoperability



Semantic

Representation

Semantic

Mapping

Semantic


Interoperability

2

Overview


The Problem


Tightness of Coupling & Explicit Semantics


Semantic Integration Implies Semantic Composition


Dimensions of Interoperability & Integration


Ontologies & the Semantic Web


The Ontology Spectrum


What are Ontologies?


Levels of Ontology Representation


What Problems do Ontologies Help Solve?


Semantic Web


Ontologies for Semantically Interoperable Systems


Enabling Semantic Interoperability


Examples


Visions


What do We Want the Future to be?

3

The problem


With the increasing complexity of our systems and our IT
needs, and the distance between systems, we need to
go toward

human level interaction


We need to
maximize
the amount of
semantics

we can
utilize and make it

increasingly

explicit


From data and information level, we need to go toward
human
semantic
level interaction

DATA

Information

Knowledge

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Human Meaning

Vehicle

Located at

Semi
-
mountainous terrain

obscured

decide

Vise maneuver


Semantic representation & semantic
interoperability/integration become very important

4

Tightness of Coupling & Semantic
Explicitness

Implicit, TIGHT

Explicit, Loose

1 System: Small Set of Developers

Local

Far

Same Process Space

Same

Address

Space

Same CPU

Same OS

Same Programming Language

Same DBMS

Same Local Area Network

Systems of Systems

Enterprise

Community

Internet

Same Wide Area Network Client
-
Server

Same Intranet

Federated DBs

Data Warehouses

Data Marts

Workflow Ontologies

Compiling

Linking

Agent Programming

Web Services: SOAP

Distributed Systems OOP

Applets

Semantic Mappings

Semantic Brokers

Looseness of Coupling

Semantics Explicitness

XML, XML Schema

Conceptual Models

RDF/S, OWL

Web Services: UDDI, WSDL

OWL
-
S

Modal Policies

Middleware Web

Peer
-
to
-
peer

N
-
Tier Architecture EAI

From Synchronous Interaction to
Asynchronous Communication

Performance = k / Integration_Flexibility

5

Semantic Integration Implies Semantic
Composition


Simple Procedure Integration &

Composition

Concatenation, alignment of calling

Procedure with called procedure:


Caller: Do_this (integer: 5, string: “sales”)

Called: Do_this (integer: X, string: Y)



Simple Syntactic Object Integration

& Composition

Alignment of embedded interface

definition language statements mapping

two CORBA, Javabean objects



Simple Semantic Model, Knowledge

Integration & Composition

Unification of tree or graph structures,

with reasoning, simple Semantic Web

ontologies:







-

signifies the composition operation



Complex Semantic Model, Knowledge,

System Integration & Composition


Unification of complex networks of graph

Structures, with complex reasoning, complex

Semantic Web ontologies:













1960

1998

2005

2010

6

Dimensions of Interoperability &
Integration

Enterprise

Object

Data

System

Application

Component

0%

100%

3 Kinds of Integration

Interoperability Scale

Our interest lies here

Community

7

Semantic Interoperability/Integration
Definition


To
interoperate

is to participate in a
common
purpose


Operation sets the context


Purpose is the intention, the end to which activity is directed


Semantics is fundamentally
interpretation


Within a particular context


From a particular point of view


Semantic Interoperability/Integration is
fundamentally driven by
communication of
purpose


Participants determined by interpreting capacity to meet
operational objectives


Service obligations and responsibilities explicitly contracted

8

weak semantics

strong semantics

Is Disjoint Subclass of
with transitivity
property

Modal Logic

Logical Theory

Thesaurus


Has Narrower Meaning Than

Taxonomy

Is Sub
-
Classification of

Conceptual Model


Is Subclass of

DB Schemas, XML Schema

UML

First Order Logic

Relational

Model, XML

ER

Extended ER

Description Logic

DAML+OIL, OWL

RDF/S

XTM

Ontology Spectrum: One View

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

9

Logical Theory

Thesaurus


Has Narrower Meaning Than

Taxonomy

Is Sub
-
Classification of

Conceptual Model


Is Subclass of

Is Disjoint Subclass of
with transitivity
property

weak semantics

strong semantics

DB Schemas, XML Schema

UML

Modal Logic

First Order Logic

Relational

Model, XML

ER

Extended ER

Description Logic

DAML+OIL, OWL

RDF/S

XTM

Ontology Spectrum: One View

Problem: Very General

Semantic Expressivity: Very High

Problem: Local

Semantic Expressivity: Low

Problem: General

Semantic Expressivity: Medium

Problem: Local

Semantic Expressivity: High

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

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Supplier A

Supplier

B

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r

Ontology

A Business Example of Ontology


11


What Problems Do Ontologies Help
Solve?



Heterogeneous database problem


Different organizational units, Service Needers/Providers have
radically different databases


Different
syntactically:

what’s the format?


Different
structurally:

how are they structured?


Different
semantically:

what do they mean?


They all speak different languages (access, description, schemas,
meaning)


Integration: rather than N
2

problem, with single, adequate Ontology
reduces to N


Enterprise
-
wide system interoperability problem


Currently: system
-
of
-
systems, vertical stovepipes


Ontologies act as conceptual model representing enterprise
consensus semantics


Relevant document retrieval/question
-
answering problem


What is the meaning of your query?


What is the meaning of documents that would satisfy your query?


Can you obtain only meaningful, relevant documents?



12

Emerging XML Stack Architecture for
the Semantic Web + Grid + Agents


Semantic Brokers


Intelligent Agents


Advanced Applications


Use, Intent: Pragmatics


Trust:

Proof +
Security

+
Identity


Reasoning/Proof

Methods


OWL
: Ontologies


RDF Schema
: Ontologies


RDF
: Instances (assertions)


XML Schema
: Encodings of Data
Elements & Descriptions, Data Types,
Local Models


XML
: Base Documents


Grid & Semantic Grid:

New System
Services, Intelligent QoS

Syntax: Data

Structure

Semantics

Higher Semantics

Reasoning/Proof

XML

XML Schema

RDF/RDF Schema

OWL

Inference Engine

Trust

Security/Identity

Use, Intent

Pragmatic Web

Intelligent Domain Services, Applications

Agents, Brokers, Policies

Grid & Semantic Grid Services

RULES

13

Semantic Web Services Stack

Adapted from: Bussler, Christoph; Dieter Fensel;
Alexander Maedche. 2003. A Conceptual
Architecture for Semantic Web Enabled Web
Services. SIGMOD Record, Dec 2002.
http://www.acm.org/sigmod/record/issues/0212/S
PECIAL/4.Bussler1.pdf.

RULES

14

Enabling Semantic Interoperability


Semantic Interoperability is enabled through:


Establishing base semantic representation via ontologies

(class
level) and their knowledge bases (instance level)


Defining semantic mappings & transformations among ontologies

(and treating these mappings as individual theories just like
ontologies)


Defining algorithms that can determine semantic similarity

and
employing their output in a semantic mapping facility that uses
ontologies


The use of ontologies & semantic mapping software can
reduce the loss of semantics (meaning) in information
exchange among heterogeneous applications
, such as:


Web Services


E
-
Commerce, E
-
Business


Enterprise architectures, infrastructures, and applications


Complex C4ISR systems
-
of
-
systems


Integrated Intelligence analysis


15

Semantic Interoperability,
Integration: Multiple Semantics


Multiple contexts, views
, application & user
perspectives


Multiple levels of precision,
specification,
definiteness required


Multiple levels of semantic model verIisimilitude
,
fidelity, granularity


Multiple kinds of semantic mappings
,
transformations needed:


Entities, Relations, Properties, Ontologies, Model Modules,
Namespaces, Meta
-
Levels, Facets (i.e., properties of properties),
Units of Measure, Conversions, etc.


Upper Ontologies will become more important


To be able to interrelate domain ontologies

16

Simple Example: Semantics of Date
Across Applications


System
1

Instance of Concept: Date
1


Attribute: YR = Int 1


Attribute: MO = String “Aug”


Attribute: DY = Int 12


System
2
: Instance of Concept = Date
2


Attribute: DayOfWeek = Sunday


Attribute: ActualDate =


String “12082001”


Semantically Equivalent? Then How?



DATE 2

DayOfWeek

ActualDate

DATE 1

MO

YR

DY

Exactly Semantically Equivalent to?

No: Approximately Semantically
Equivalent to. So Mappings and
Transformations are Needed!

Add Assertions, Apply
Transformations
(directional)

Once Assertions, Transformations
Defined: become part of Integration
Ontology & Reused

Date2.ActualDate


䑡瑥1⹄.


䑡瑥1⹍传


䑡瑥1⹙R

17

Simple Example: Semantics

of Location Across Applications


System
1

Instance of Concept: Location
1


Attribute: SourceDeadReckoning = A


Attribute: SourceDRLatitude = B


Attribute: SourceDRLongitude = C


Attribute: TargetDRBearingLine = D


Attribute: TargetDRAltitude = E


Attribute: ActualMeasuredAltitude = F


Attribute: PositionLine = G


System
2
: Instance of Concept: Location
2


Attribute: Address = H


Attribute: City = I


Attribute: StateProvince = J


Attribute: Country = K


Attribute: MailCode = L

Approximately
Semantically
Equivalent to?

18

Electronic Commerce Example:

One Company

Products

Metal

Health

Electronic

Chemical

Distributor

Manufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld

iMicro

3Initial

Location

Africa

Europe

Spain

Portugal

Asia

Time

Point

Interval

Coordinate

System

UTM

Geographic

LatLong

GPS

UnitOfMeasure

Distance

Mass

Liquid

Solid

Shipping

Methods

Air

Ground

Truck

RegionalCarrier

LocalCarrier

Sea

Applications

TradingHub

RFI/RFQ

Sell

ShippedBy

ObtainedFrom

LocatedAt

GivenBy

MeasuredBy

Uses

Support

AvailableAt

Train

19

Now Assume Each Company Has Separate Enterprise
Semantics, Multiply by the Number of Companies, & Have
Them Interoperate and Preserve Semantics

Try doing this without Ontologies! You can, but it’s a Nightmare, and it COSTS: Now & Later!

Products

Metal

Health

Electronic

Chemical

Distributor

Manufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld

iMicro

3Initial

Location

Africa

Europe

Spain

Portugal

Asia

Time

Point

Interval

Coordinate

System

UTM

Geographic

LatLong

GPS

UnitOfMeasure

Distance

Mass

Liquid

Solid

Shipping

Methods

Air

Ground

Truck

RegionalCarrier

LocalCarrier

Sea

Applications

TradingHub

RFI/RFQ

Sell

ShippedBy

ObtainedFrom

LocatedAt

GivenBy

MeasuredBy

Uses

Support

AvailableAt

Train

Products

Metal

Health

Electronic

Chemical

Distributor

Manufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld

iMicro

3Initial

Location

Africa

Europe

Spain

Portugal

Asia

Time

Point

Interval

Coordinate

System

UTM

Geographic

LatLong

GPS

UnitOfMeasure

Distance

Mass

Liquid

Solid

Shipping

Methods

Air

Ground

Truck

RegionalCarrier

LocalCarrier

Sea

Applications

TradingHub

RFI/RFQ

Sell

ShippedBy

ObtainedFrom

LocatedAt

GivenBy

MeasuredBy

Uses

Support

AvailableAt

Train

Products

Metal

Health

Electronic

Chemical

Distributor

Manufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld

iMicro

3Initial

Location

Africa

Europe

Spain

Portugal

Asia

Time

Point

Interval

Coordinate

System

UTM

Geographic

LatLong

GPS

UnitOfMeasure

Distance

Mass

Liquid

Solid

Shipping

Methods

Air

Ground

Truck

RegionalCarrier

LocalCarrier

Sea

Applications

TradingHub

RFI/RFQ

Sell

ShippedBy

ObtainedFrom

LocatedAt

GivenBy

MeasuredBy

Uses

Support

AvailableAt

Train

Products

Metal

Health

Electronic

Chemical

Distributor

Manufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld

iMicro

3Initial

Location

Africa

Europe

Spain

Portugal

Asia

Time

Point

Interval

Coordinate

System

UTM

Geographic

LatLong

GPS

UnitOfMeasure

Distance

Mass

Liquid

Solid

Shipping

Methods

Air

Ground

Truck

RegionalCarrier

LocalCarrier

Sea

Applications

TradingHub

RFI/RFQ

Sell

ShippedBy

ObtainedFrom

LocatedAt

GivenBy

MeasuredBy

Uses

Support

AvailableAt

Train

20

Semantic Issues: Complexity


An ontology allows for near linear semantic integration (actually
2n
-

1) rather than near n
2

(actually n
2

-

n) integration


Each application/database maps to the "lingua franca" of the ontology,
rather than to each other


A

C

A

B

B

C

A

C

B

Ordinary Integration

Ontology Integration

A

D

B

D

C

D

Add D:

Add D:

A

D

A

B

C

D

B

C

A

D





2 Nodes

3 Nodes

4 Nodes

5 Nodes

2 Edges

6 Edges

12 Edges

20 Edges

2 Nodes

3 Nodes

4 Nodes

5 Nodes

2
Edges

4
Edges

6

Edges

8

Edges

21

Measures of Semantic Similarity


Synonyms: Approximation, Similarity


Syntactic Methods


Formal & Logical Methods: Inference


Graph Theory


Information Theory, Probability


Semantic Methods


Formal Concept Analysis


Possible Worlds Semantics: Close to Far Accessible Worlds


Accessibility relation usually taken to be entailment


Hybrid Syntactic + Semantic Methods


Category Theory


Anchored Concepts + Graph Theory


Approximation & plausibility methods


Computational linguistics: corpus statistical measures + NL
semantics


Symbolic to numeric/stochastic/continuous semantic
conversions


22

Graph Homomorphism/Analogy?

With Anchoring?


In general, solutions based on graph homomorphisms won’t work:
structural correspondence does not ensure semantic correspondence


hammer

handgun

claw

pistol

revolver

sledge

automatic

semi
-
automatic

single
-
action

fiberglass claw

6 lb.

12 lb.

NOT Semantically Equivalent

hammer

hand tool

claw

pliers

sledge

carpenter

drilling

linesman

fiberglass claw

6 lb.

12 lb.

Approximately

Semantically Equivalent


But, with some semantic “anchoring”, structure may help with semantics

steel hammer

ANCHOR

23

Ontology Mapping: Namespaces,
Contexts, Lattice of Theories

Ontology2
Namespace1

Top of Lattice of Theories

Ontology1
Namespace1

Context2

Context1

Namespace2

Ontology3 Namespace1

24

Ontology Mappings & Context


Initial Ontology

Mapped Ontologies

Context 1

or

Subdomain

Theory 1

“training”

Context

2
or

Subdomain

Theory 2

“orbital”

Context

3
or

Subdomain

Theory 3

“ground”

altitude

Whatever it usually or

Canonically “means”,

I.e., relationships, subclasses,

constraints

Whatever it specifically


“means”, I.e., relationships,


subclasses, constraints

Whatever it specifically


“means”, I.e., relationships,


subclasses, constraints

Whatever it specifically


“means”, I.e., relationships,


subclasses, constraints

What “altitude” means

In the context of training

What “altitude” means

In the context of orbital

What “altitude” means

In the context of ground

Context

4
or

Subdomain

Theory 4

“Air Force”

Whatever it specifically


“means”, I.e., relationships,


subclasses, constraints

What “altitude” means

In the context of orbital

And Air Force

Context

5
or

Subdomain

Theory 5

“Navy”

What “altitude” means

In the context of ground

And Navy

tank



Contexts as Domain


Theories/Ontologies



Elaborate based on need



Compose on fly

Whatever it specifically


“means”, I.e., relationships,


subclasses, constraints

25

Vision:

Semantic Broker

Web
-
Based

Machine
-
Interpretable

Semantics

(stacked languages)

Use/Intent

Proof

OWL

Agent Services

Web Services


RDF/S

XTM

XLT


Specific

XML

Languages


XML

Schema


XML


Schema

Application

Application

Data

Mappings

Mappings

Ontologies

Documents

Application

Schema

Application

Application

Data

Application

Schema

Application

Application

Data

Application

Semantic Broker

Semantic

Mapper

Contexts






Requests






Services

26

Vision: Semantically Interoperable
Systems

Semantic Broker

Active

Application

Agent

Active

Application

Agent

Active

Application

Agent

Application

Application

Application

Users:

Purchasers,
Sellers,
Decision
-
Makers

Consumers,
Analysts,
Manufacturers

Application

Meta
-
data

Agency

Meta
-
data

Meta
-
Knowledge

Upper Ontology: Generic Base

Organizations

Interaction

Knowledge

Workflow

Processes

Mapping

Knowledge

Products & Svcs

Ontologies

Fielded

Systems

Semantic

Mappings

Queries

Ontology and Reasoning
Services

Databases

Documents

27

What do we want the future to be?


2100 A.D: models, models, models


There are no human
-
programmed programming languages


There are only Models

Ontological Models

Knowledge Models

Belief Models

Application Models

Presentation Models

Target Platform Models

Transformations,
Compilations

Executable Code

I
N
F
R
A
S
T
R
U
C
T
U
R
E

28

Thank You!

Questions? lobrst@mitre.org

Shameless Plug:


The Semantic Web: The Future of XML, Web Services, and Knowledge
Management,
--

Mike Daconta, Leo Obrst, & Kevin Smith, Wiley, June,
2003


http://www.amazon.com/exec/obidos/ASIN/0471432571/qid%3D10502646
00/sr%3D11
-
1/ref%3Dsr%5F11%5F1/103
-
0725498
-
4215019

Contents
:

1.
What is the Semantic Web?

2.
The Business Case for the Semantic Web

3.
Understanding XML and its Impact on the Enterprise

4.
Understanding Web Services

5.
Understanding the Resource Description Framework

6.
Understanding the Rest of the Alphabet Soup

7.
Understanding Taxonomies

8.
Understanding Ontologies

9.
Crafting Your Company’s Roadmap to the Semantic Web