Ontologies for Semantically Interoperable Systems
Dr. Leo Obrst
MITRE
Center for Innovative Computing & Informatics
Information Semantics
Lobrst@mitre.org
January 15, 2004

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?

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

Tightness of Coupling & Semantic Explicitness

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

Semantic Integration Implies Semantic Composition

Dimensions of Interoperability & Integration

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

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Architecture: Ontology & Applications


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 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?

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

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.

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?

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

Electronic Commerce Example:
One Company

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

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

Semantic Web Services Stack

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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

Vision:
Semantic Broker

Vision: Semantically Interoperable  Systems

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

Contact
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%3D1050264600/sr%3D11-1/ref%3Dsr%5F11%5F1/103-0725498-4215019
Contents:
What is the Semantic Web?
The Business Case for the Semantic Web
Understanding XML and its Impact on the Enterprise
Understanding Web Services
Understanding the Resource Description Framework
Understanding the Rest of the Alphabet Soup
Understanding Taxonomies
Understanding Ontologies
Crafting Your Company’s Roadmap to the Semantic Web