Copyright 2004 by The MITRE Corporation 1 Dr. Leo Obrst MITRE Center for Innovative Computing &...

Copyright 2004 by The MITRE Corporation

1

Dr. Leo ObrstDr. Leo ObrstMITRE MITRE

Center for Innovative Computing & InformaticsCenter for Innovative Computing & InformaticsInformation SemanticsInformation Semantics

[email protected]@mitre.orgApril 21, 2023April 21, 2023

Ontologies & the Semantic Web Ontologies & the Semantic Web for Semantic Interoperabilityfor Semantic Interoperability

SemanticRepresentation

SemanticMapping

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

Run84

ID=08

NULLPARRT

ACC

ID=34

e

5

&

#

~

Qü

@

¥

¥

Æ�

Å

Tank

¥

Noise Human Meaning

VehicleLocated at

Semi-mountainous terrainobscured

decide

Vise maneuver

• Semantic representation & semantic interoperability/integration become very important

4

Tightness of Coupling & Semantic Explicitness

Data

Application

Implicit, TIGHT

Explicit, Loose

1 System: Small Set of DevelopersLocal

Far

Same Process Space

Same Address Space

Same CPUSame 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 WarehousesData Marts

Workflow Ontologies

Compiling

Linking

Agent Programming

Web Services: SOAP

Distributed Systems OOP

Applets

Semantic MappingsSemantic Brokers

Looseness of Coupling

Se

ma

nti

cs

Ex

plic

itn

ess

XML, XML Schema

Conceptual Models

RDF/S, OWLWeb 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 &CompositionConcatenation, 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& CompositionAlignment of embedded interface definition language statements mapping two CORBA, Javabean objects

Simple Semantic Model, Knowledge Integration & CompositionUnification of tree or graph structures,with reasoning, simple Semantic Webontologies:

- 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

20052010

6

Dimensions of Interoperability & Integration

Enterprise

Object

Data

System

Application

Component

0% 100%

6 Levels o

f Inte

ropera

bility

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

8weak semanticsweak semantics

strong semanticsstrong semantics

Is Disjoint Subclass of with transitivity property

Modal Logic

Logical Theory

Thesaurus Has Narrower Meaning Than

TaxonomyIs Sub-Classification of

Conceptual Model Is Subclass of

DB Schemas, XML Schema

UML

First Order Logic

RelationalModel, XML

ER

Extended ER

Description LogicDAML+OIL, OWL

RDF/SXTM

Ontology Spectrum: One View

Syntactic Interoperability

Structural Interoperability


9

Logical Theory

Thesaurus Has Narrower Meaning Than

TaxonomyIs Sub-Classification of

Conceptual Model Is Subclass of

Is Disjoint Subclass of with transitivity property

weak semanticsweak semantics

strong semanticsstrong semantics

DB Schemas, XML Schema

UML

Modal LogicFirst Order Logic

RelationalModel, XML

ER

Extended ER

Description LogicDAML+OIL, OWL

RDF/SXTM

Ontology Spectrum: One View

Problem: Very GeneralSemantic Expressivity: Very High

Problem: Local Semantic Expressivity: Low

Problem: GeneralSemantic Expressivity: Medium

Problem: Local Semantic Expressivity: High

Syntactic Interoperability

Structural Interoperability


10

.251.25SquareXAB035

.751.5RoundXAB023

…Price ($US)

Size (in)

ShapeCatalog No.

.4531S550298

.3537R550296

…Price ($US)

Diam (mm)

Geom.Part No.

Washer

Catalog No.Shape Size Price

iMetal Corp.E-Machina

iMetal Corp.E-Machina

Manufacturer

.451.25Square550298

.351.5Round550296

.751.5RoundXAB023

.251.25SquareXAB035

…Price ($US)

Size (in)

ShapeMfr No.

Supplier ASupplier

B

Buyer

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 N2 problem, with single, adequate Ontology reduces to N

Enterprise-wide system interoperability problemEnterprise-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

RU

LE

S

13

Semantic Web Services Stack

OWL, OWL-S, OWL-Rules

Service Entities, Relations, Rules

RDF/S Service Instances

BPEL4WS (Business Process Execution Language for Web Services)

Service Flow & Composition

Trading Partner Agreement

Service Agreement

UDDI/WS Inspection

Service Discovery (focused & unfocused)

UDDI Service Publication

WSDL Service Description

WS Security Secure Messaging

SOAP XML Messaging

HTTP, FTP, SMTP, MQ, RMI over IIOP

Transport

Sem

anti

cs

Pra

gm

atic

s

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/SPECIAL/4.Bussler1.pdf.

RU

LE

S

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• System1 Instance of Concept: Date1

– Attribute: YR = Int 1– Attribute: MO = String “Aug”– Attribute: DY = Int 12

• System2: Instance of Concept = Date2

– 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 Date1.DY Date1.MO Date1.YR

17

Simple Example: Semantics of Location Across Applications

• System1 Instance of Concept: Location1

– Attribute: SourceDeadReckoning = A– Attribute: SourceDRLatitude = B– Attribute: SourceDRLongitude = C– Attribute: TargetDRBearingLine = D– Attribute: TargetDRAltitude = E– Attribute: ActualMeasuredAltitude = F– Attribute: PositionLine = G

• System2: Instance of Concept: Location2

– 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

MetalHealthElectronic Chemical

DistributorManufacturer

Wholesaler

Retailer

EndRun

TradingPartners

TransWorld iMicro

3InitialLocation

Africa Europe

SpainPortugal

Asia

Time

Point Interval

CoordinateSystem

UTMGeographic

LatLongGPS

UnitOfMeasure

DistanceMass

Liquid Solid

ShippingMethods

AirGround

Truck

RegionalCarrierLocalCarrier

Sea

ApplicationsTradingHub RFI/RFQ

Sell

ShippedBy

ObtainedFrom LocatedAt

GivenBy

MeasuredBy

UsesSupport

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

Products



Wholesaler

Retailer

EndRun

TradingPartners

TransWorld iMicro

3InitialLocation

Africa Europe

SpainPortugal

Asia

Time

Point Interval

Coordinate

System

UTMGeographic

LatLongGPS

UnitOfMeasure

DistanceMass

LiquidSolid

Shipping

MethodsAirGround

Truck


Sea


Sell

ShippedBy


GivenBy

MeasuredBy

UsesSupport

AvailableAt

Train

Products



Wholesaler

Retailer

EndRun

TradingPartners

TransWorld iMicro

3InitialLocation

Africa Europe

SpainPortugal

Asia

Time

Point Interval

Coordinate

System

UTMGeographic

LatLongGPS

UnitOfMeasure

DistanceMass

LiquidSolid

Shipping

MethodsAirGround

Truck


Sea


Sell

ShippedBy


GivenBy

MeasuredBy

UsesSupport

AvailableAt

Train

Products



Wholesaler

Retailer

EndRun

TradingPartners

TransWorld iMicro

3InitialLocation

Africa Europe

SpainPortugal

Asia

Time

Point Interval

Coordinate

System

UTMGeographic

LatLongGPS

UnitOfMeasure

DistanceMass

LiquidSolid

Shipping

MethodsAirGround

Truck


Sea


Sell

ShippedBy


GivenBy

MeasuredBy

UsesSupport

AvailableAt

Train

Products



Wholesaler

Retailer

EndRun

TradingPartners

TransWorld iMicro

3InitialLocation

Africa Europe

SpainPortugal

Asia

Time

Point Interval

Coordinate

System

UTMGeographic

LatLongGPS

UnitOfMeasure

DistanceMass

LiquidSolid

Shipping

MethodsAirGround

Truck


Sea


Sell

ShippedBy


GivenBy

MeasuredBy

UsesSupport

AvailableAt

Train

20

Semantic Issues: Complexity

• An ontology allows for near linear semantic integration (actually 2n - 1) rather than near n2 (actually n2 - 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 CB

Ordinary Integration Ontology Integration

A DB DC 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 pistolrevolversledge

automaticsemi-automaticsingle-actionfiberglass claw 6 lb. 12 lb.

NOT Semantically Equivalent

hammer hand tool

claw plierssledge

carpenter drillinglinesmanfiberglass claw 6 lb. 12 lb.

Approximately Semantically Equivalent

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

steel hammerANCHOR

23

Ontology Mapping: Namespaces, Contexts, Lattice of Theories

Ontology2 Namespace1

Top of Lattice of TheoriesOntology1 Namespace1

Context2Context1

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



What “altitude” meansIn the context of training

What “altitude” meansIn the context of orbital

What “altitude” meansIn the context of ground

Context 4 or Subdomain Theory 4“Air Force”


What “altitude” meansIn the context of orbitalAnd Air Force

Context 5 or Subdomain Theory 5“Navy”

What “altitude” meansIn the context of groundAnd Navy

tank

• Contexts as Domain Theories/Ontologies• Elaborate based on need• Compose on fly


25

Vision:Semantic Broker

Web-Based Machine-Interpretable

Semantics(stacked languages)

Use/IntentProofOWL

Agent ServicesWeb Services

RDF/SXTMXLT

SpecificXML

Languages

XMLSchema

XML

Schema

Application

Application

Data

Mappings Mappings

Ontologies

Documents

Application

Schema

Application

Application

Data

Application

Schema

Application

Application

Data

Application

Semantic Broker

SemanticMapper

Contexts

Req

ues

tsS

ervi

ces

26

Vision: Semantically Interoperable Systems

Semantic Broker

ActiveApplication

Agent

ActiveApplication

Agent

ActiveApplication

Agent

Application ApplicationApplication

Users:Purchasers, Sellers, Decision-MakersConsumers, Analysts, Manufacturers

ApplicationMeta-data

AgencyMeta-data

Meta-Knowledge

Upper Ontology: Generic Base

Organizations

Interaction Knowledge

WorkflowProcesses

Mapping Knowledge

Products & Svcs

Ontologies

FieldedSystems

SemanticMappings

Queries

Ontology and Reasoning Services

DatabasesDocuments

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

INFRASTRUCTURE

28

Thank You!

Questions? [email protected] Plug:

The Semantic Web: The Future of XML, Web Services, and Knowledge Management, -- Mike Daconta, Leo Obrst, & Kevin Smith, Wiley, June, 2003http://www.amazon.com/exec/obidos/ASIN/0471432571/qid%3D1050264600/sr%3D11-1/ref%3Dsr%5F11%5F1/103-0725498-4215019

Contents: 1. What is the Semantic Web?2. The Business Case for the Semantic Web3. Understanding XML and its Impact on the Enterprise4. Understanding Web Services5. Understanding the Resource Description Framework6. Understanding the Rest of the Alphabet Soup7. Understanding Taxonomies8. Understanding Ontologies9. Crafting Your Company’s Roadmap to the Semantic Web

Copyright 2004 by The MITRE Corporation 1 Dr. Leo Obrst MITRE Center for Innovative Computing &...

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Transcript of Copyright 2004 by The MITRE Corporation 1 Dr. Leo Obrst MITRE Center for Innovative Computing &...