copyright 2004 by the mitre corporation 1 dr. leo obrst mitre center for innovative computing &...
TRANSCRIPT
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
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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?
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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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• Semantic representation & semantic interoperability/integration become very important
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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
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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
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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
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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
Semantic Interoperability
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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
Semantic Interoperability
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Size (in)
ShapeCatalog No.
.4531S550298
.3537R550296
…Price ($US)
Diam (mm)
Geom.Part No.
Washer
Catalog No.Shape Size Price
iMetal Corp.E-Machina
iMetal Corp.E-Machina
Manufacturer
.451.25Square550298
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…Price ($US)
Size (in)
ShapeMfr No.
Supplier ASupplier
B
Buyer
Ontology
A Business Example of Ontology
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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?
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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
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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
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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
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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
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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
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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?
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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
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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
MetalHealthElectronic Chemical
DistributorManufacturer
Wholesaler
Retailer
EndRun
TradingPartners
TransWorld iMicro
3InitialLocation
Africa Europe
SpainPortugal
Asia
Time
Point Interval
Coordinate
System
UTMGeographic
LatLongGPS
UnitOfMeasure
DistanceMass
LiquidSolid
Shipping
MethodsAirGround
Truck
RegionalCarrierLocalCarrier
Sea
ApplicationsTradingHub RFI/RFQ
Sell
ShippedBy
ObtainedFrom LocatedAt
GivenBy
MeasuredBy
UsesSupport
AvailableAt
Train
Products
MetalHealthElectronic Chemical
DistributorManufacturer
Wholesaler
Retailer
EndRun
TradingPartners
TransWorld iMicro
3InitialLocation
Africa Europe
SpainPortugal
Asia
Time
Point Interval
Coordinate
System
UTMGeographic
LatLongGPS
UnitOfMeasure
DistanceMass
LiquidSolid
Shipping
MethodsAirGround
Truck
RegionalCarrierLocalCarrier
Sea
ApplicationsTradingHub RFI/RFQ
Sell
ShippedBy
ObtainedFrom LocatedAt
GivenBy
MeasuredBy
UsesSupport
AvailableAt
Train
Products
MetalHealthElectronic Chemical
DistributorManufacturer
Wholesaler
Retailer
EndRun
TradingPartners
TransWorld iMicro
3InitialLocation
Africa Europe
SpainPortugal
Asia
Time
Point Interval
Coordinate
System
UTMGeographic
LatLongGPS
UnitOfMeasure
DistanceMass
LiquidSolid
Shipping
MethodsAirGround
Truck
RegionalCarrierLocalCarrier
Sea
ApplicationsTradingHub RFI/RFQ
Sell
ShippedBy
ObtainedFrom LocatedAt
GivenBy
MeasuredBy
UsesSupport
AvailableAt
Train
Products
MetalHealthElectronic Chemical
DistributorManufacturer
Wholesaler
Retailer
EndRun
TradingPartners
TransWorld iMicro
3InitialLocation
Africa Europe
SpainPortugal
Asia
Time
Point Interval
Coordinate
System
UTMGeographic
LatLongGPS
UnitOfMeasure
DistanceMass
LiquidSolid
Shipping
MethodsAirGround
Truck
RegionalCarrierLocalCarrier
Sea
ApplicationsTradingHub RFI/RFQ
Sell
ShippedBy
ObtainedFrom LocatedAt
GivenBy
MeasuredBy
UsesSupport
AvailableAt
Train
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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
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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
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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
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Ontology Mapping: Namespaces, Contexts, Lattice of Theories
Ontology2 Namespace1
Top of Lattice of TheoriesOntology1 Namespace1
Context2Context1
Namespace2
Ontology3 Namespace1
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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” 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”
Whatever it specifically “means”, I.e., relationships, subclasses, constraints
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
Whatever it specifically “means”, I.e., relationships, subclasses, constraints
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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
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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
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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
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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