Copyright 2003 by The MITRE Corporation 1 Dr. Leo Obrst MITRE Center for Innovative Computing & Informatics Information Semantics [email protected] February

Copyright 2003 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 10, 2023April 10, 2023

Ontologies for Semantically Ontologies for Semantically Interoperable SystemsInteroperable Systems

SemanticRepresentation

SemanticMapping

Semantic Interoperability

2

Overview

• The Problem• Tightness of Coupling & Explicit Semantics• Semantic Integration Implies Semantic Composition• Dimensions of Interoperability & Integration• Ontologies

– The Ontology Spectrum– What are Ontologies?– Levels of Ontology Representation– What Problems do Ontologies Help Solve?

• 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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Noise Human Meaning

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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 CPU

Same OS

Same Programming Language

Same DBMS

Same Local Area Network

Systems of Systems

Enterprise

Community

Internet

Same Wide Area Network

Same Intranet

Federated DBs

Data WarehousesData Marts

Ontologies

Linking

Libraries

OOP

Agent ProgrammingWeb Services: SOAP

Distributed Systems

Applets

Semantic Mappings Semantic Brokers

Looseness of Coupling

Se

ma

nti

cs

Ex

plic

itn

ess From Local, Tight, Implicit

To Far, Loose, Explicit

XMLConceptual Models

RDF/S, OWL

Web Services: UDDI, WSDL

OWL-S

Modal Policies

5

Semantic Interoperability: Tight to Loose Coupling

• Tight coupling: applies to databases, systems– Same address space, same process space, same operating system,

same machine– Semantic compacts can be made because semantics stays in the

minds of the developers who agree

• Loose coupling– Different platforms, networks, anywhere on Internet– Semantics must be explicit: agents, programs need to interpret the

semantics directly, to interoperate semantically– Levels: systems of systems, enterprise, community, value

chains/pipes

• Ontologies (explicitly represented, logical semantics): increasingly needed the higher you go

6

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

7

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

8

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

9weak 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


10

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


11

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Size (in)

ShapeCatalog No.

.4531S550298

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…Price ($US)

Diam (mm)

Geom.Part No.

Washer

Catalog No.Shape Size Price

iMetal Corp.E-Machina

iMetal Corp.E-Machina

Manufacturer

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…Price ($US)

Size (in)

ShapeMfr No.

Supplier ASupplier

B

Buyer

Ontology

A Business Example of Ontology

12

Architecture: Ontology & Applications

Ontology Layer

Ontology Application Services Layer

Application Layer

User Interface Layer

SemanticRepresentationRequirements

User (& presentation)Requirements

Support for Userto RepresentationRequirements

Search Transact

User RolesBuyer (Engr., Analyst)Seller

Search Transact Navigate Get/Put Data

Make/Get Alias Look-up Contextualize Infer

Taxonomies Metadata Attributes

In the emerging Web Services paradigm, Levels 1 - 4 consist of composable services

1

2

3

4

13

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?

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 verisimilitude, 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.

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

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, DAML+OIL: 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

Sem-Grid Services Water, LISP?

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

21

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

Adapted from: Bussler, Christoph; Dieter Fensel; Alexander Maedche,. 2003. A Conceptual Architecture for Semantic Web Enabled Web Services.

Sem

anti

cs

Pra

gm

atic

s

22

Z

Y X W

V T S

AM

I J

B C D E

F G H

Simple, Informal E-Commerce

Application Taxonomy

(Reference) Ontology

Well-defined subclass relationOther ontological relationsIll-defined parent-child relation Mappings

Industrial process

Products of the process

Equipment usedIn the process

Employees involved in the process

Generated from

Industrial process

Specific product

Generalproduct

Semantic Mappings

23

Electronics Namespace

UNSPSC Namespace

Ontology

Ontology nodeUNSPSC nodeInheritance (subclass)

Taxonomic Standard to Ontology Mapping: e.g., Web Services

UNSPSC Mapped to Electronics Domain OntologyUse of Nebenstruktur (shadow structure)

24

Z

Y

X WV T S

AM

I J

B C D E

F G H

Ontology subclass relationOther ontological relationsApplication subclass relation Mappings (equivalence)

Implementing Mappings: Semantic Model of Application in the Ontology• Model of Application: All ontology-relevant application structures are

included in the ontology model– Create mapping relation from an ontology node to nodes in the application

model

Claw hammer

Hammer as used in carpenter’s catalog X

25

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

26

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

27

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

28

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

29

Contact

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

30

Backup

31

Ontology & Ontologies 1

• An ontology defines the terms used to describe and represent an area of knowledge (subject matter)

– An ontology also is the model (set of concepts) for the meaning of those terms

– An ontology thus defines the vocabulary and the meaning of that vocabulary

• Ontologies are used by people, databases, and applications that need to share domain information

– Domain: a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc.

• Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them

– They encode domain knowledge (modular)

– Knowledge that spans domains (composable)

– Make knowledge available (reusable)

32


• The term ontology has been used to describe models with different degrees of structure (Ontology Spectrum)

– Less structure: Taxonomies (Semio taxonomies, Yahoo hierarchy, biological taxonomy), Database Schemas (many) and metadata schemes (ICML, ebXML, WSDL)

– More Structure: Thesauri (WordNet, CALL, DTIC), Conceptual Models (OO models, UML)

– Most Structure: Logical Theories (Ontolingua, TOVE, CYC, Semantic Web)

• Ontologies are usually expressed in a logic-based language

– Enabling detailed, sound, meaningful distinctions to be made among the classes, properties, & relations

– More expressive meaning but maintain “computability”

• Using ontologies, tomorrow's applications can be "intelligent”

– Work at the human conceptual level

• Ontologies are usually developed using special tools that can model rich semantics

33


• Ontologies are developed by a team

– Domain Experts: have the domain knowledge

– Ontologists: know how to formally model knowledge, semantics

• On-going research investigates semi-automation of ontology development

– State-of-art for quite some time will be semi-automation

– Humans have rich semantic models & understanding, machines poor so far

– Want our machines to interact more closely at human concept level

– The more & richer the knowledge sources developed & used, the easier it gets (bootstrapping, learning)

• Rigorous ontology development methodologies evolving (e.g., Methontology)

• Tools emerging to assist domain experts in building ontologies (OntoClean)

34

Axioms, Inference Rules, Theorems, Theory

Theory

Theorems

Licensed by a valid proof using inference rules

Possible other theorems (as yet unproven)

Axioms

35

Axioms Inference Rules TheoremsClass(Thing)

Class(Person)

Class(Parent)

Class(Child)

If SubClass(X, Y) then X is a subset of Y. This also means that if A is a member of Class(X), then A is a member of Class(Y)

SubClass(Person, Thing)

SubClass(Parent, Person)

SubClass(Child, Person)

ParentOf(Parent, Child)

NameOf(Person, String)

AgeOf(Person, Integer)

If X is a member of Class (Parent) and Y is a member of Class(Child), then (X Y)

And-introduction: given P, Q, it is valid to infer P Q.

Or-introduction: given P, it is valid to infer P Q.

And-elimination: given P Q, it is valid to infer P.

Excluded middle: P P (i.e., either something is true or its negation is true)

If P Q are true, then so is P Q.

If X is a member of Class(Parent), then X is a member of Class(Person).

If X is a member of Class(Child), then X is a member of Class(Person).

If X is a member of Class(Child), then NameOf(X, Y) and Y is a String.

If Person(JohnSmith), then ParentOf(JohnSmith, JohnSmith).

36

Ontology Representation Levels

Level Example Constructs Knowledge Representation (KR) Language (Ontology Language) Level:

Meta Level to the Ontology Concept Level

Class, Relation, Instance, Function, Attribute, Property, Constraint, Axiom, Rule

Ontology Concept (OC) Level:

Object Level to the KR Language Level, Meta Level to the Instance Level

Person, Location, Event, Parent, Hammer, River, FinancialTransaction, BuyingAHouse, Automobile, TravelPlanning, etc.

Ontology Instance (OI) Level:

Object Level to the Ontology Concept Level

Harry X. Landsford III, Ralph Waldo Emerson, Person560234, PurchaseOrderTransactionEvent6117090, 1995-96 V-6 Ford Taurus 244/4.0 Aerostar Automatic with Block Casting # 95TM-AB and Head Casting 95TM

37

E-commerceArea ofInterestMostly This

Middle Ontology(Domain-spanning

Knowledge)

Most General Thing

Upper Ontology(Generic Common

Knowledge)Products/Services

Processes

Organizations

Locations

Lower Ontology(individual domains)

Metal PartsArt Supplies

Lowest Ontology(sub-domains)

Washers

But Also This!

Ontology: General Picture at Object Level