integration, interoperability and collaboration: semantic interoperability and ontology systems...

Integration, Interoperability and Collaboration:Semantic Interoperability and Ontology Systems


Fifth Semantic Interoperability for E-government Conference

MITREMcClean, VA

October 11, 2006

Presented byMatthew K. Hettinger, CEO and Chief Architect

Mathet Consulting, Inc.

Integrated, Interoperable and Collaborative Systems: Architecture and Engineering (IICSAE™)

August 15, 2006 Copyright (C) 2006 Mathet Consulting, Inc. 2

AbstractAbstract

• Characterization of the semantic interoperability problem– Levels of semantic interoperability– Measures– Collaborations

• Characterization and utilization of ontology and semantic interoperability systems to address the semantic interoperability problem

– Open world semantics, business rules, innovation– Use and implementation of standards– Interoperability systems– Systems-based architecture

• Relationships between semantic interoperability , ontology, information quality, service-oriented architecture and (inter-) enterprise system architecture including reference models

– Semantic Interoperability as a service– System integration, interoperability and collaboration– System emergence and downward (and upward) causation

• Using ontology and semantic interoperability systems (e.g. in government)– Mapping law / statute to enterprise services– Strategic, Tactical, Operational Levels of use– Inter-enterprise Collaborations

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This presentation will address the following:


Background and ContextBackground and Context

• General Systems Theory and the ‘Modeling Discipline’

• (Inter-) Enterprise Systems Architecture and Engineering

• Integration, Interoperability and Collaboration

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Semantic Interoperability and Ontology Systems are presented from the perspective of:

applied to e-Government.

This presentation is modified from an earlier presentation at the Aug 15th Summer Expedition Workshop. It overlaps a discussion of SOA and EA onto Semantic Interoperability.

Semantic Interoperability Problem Characterization

Semantic Interoperability Problem Characterization




Semantic Interoperability – Problem CharacterizationSemantic Interoperability – Problem Characterization

Semantics Interoperability

SemanticInteroperability

Working together at the semantic level of communication(s)The ability for two or more systems to interoperate at the semantic level

Requires an alignment of intended meaning and interpretation among interacting systems

Requires a semantic systems layer underlying and supporting communications- Requires Logic -

Working together- The ability of two or more systems to communicate and work together to coordinate and execute their respective services.

(The study of) Meaning in Language-The assignment of meaning to symbol sets Intended Meaning The expression of meaning with languages-The extraction / discovery of meaning from symbol sets Interpretation in a formal way -(especially for machine-machine interactions)

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System of Interest

Modeling Systems

Modeled Systems

Model Systems Modeled

Systems

System theoretic modification ofthe Semiotic Triangle(the Meaning Triangle) Product

(a subset of modeled systems)(including KID)

Resources(including KID)

Di Do

Sets of symbols (including terms), Sets of definitionsSets of relations and functions between / among symbols, definitions, things.Sets of languages (including OWL, ODM standards)axioms, rules, models, etc reflects a semiotic (semantic) state-space at any given point in time, the system, S is in a given semiotic (semantic) state

Systems of Interest has•Mission / Vision•Goals and Objectives•Role

S

Task KID In(collaboration)

Task KID Out

e.g. any government organization / agency- A government agency may be considered an enterprise system

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sSemantic Interoperability – Problem CharacterizationSemantic Interoperability – Problem Characterization



Modeling Systems

Modeled Systems

Model Systems

Modeled Systems

Di

Di

Di

DO

DO

DO

Di

DO

Each (Sub) System reflects a semiotic (semantic) state-space

At any given point in time, each (sub) system is in a given semiotic (semantic) state

Each (Sub) System of Interest has•Mission / Vision•Goals and Objectives•Role

Product(a subset of

modeled systems)(including KID)

Resources(including

KID)

Task KID In

Task KID Out

Di

DO

A

B

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tem

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1

23

4

1

-Step in process of data flow in a static or dynamic collaboration among 3 (sub) systems offering their (sub) services-- The collaboration reflects a virtual subsystem of S offering a service

S1

S2

S3

S



Data {Language: syntax, vocabulary}

Sender

Context’

Reciever

SenderReciever

System MSystem M’

Role N’Role N

Context

Conduit / Channel / MediaReceive

Data

ProcessData

Make Decisions

Take ActionsReceive

Data

ProcessData

Make Decisions

Take Actions


Sender Reciever

SenderReciever

System MSystem M’

Role N’Role N

Context


Data

ProcessData

Make Decisions

Take ActionsReceive

Data

ProcessData

Make Decisions

Take Actions

A

B

Role System(May be human or machine)

KID

KID

Has temporal / spatial, pragmatic components

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Provide a service

Provide a service


Semantic Interoperability – Problem CharacterizationSemantic Interoperability – Problem CharacterizationIn

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Behavior

Percepts

Language(s): L*

Internal Models: M*(e.g interpretations)

Reasoning

Context

Data

Data

Context DataReceive Data

Take Actions

Internal Storage

e.g. Send Data

Sensors

Effectors

His

tori

cal

Kno

wle

dge,

In

form

atio

n,

Dat

aF

acts

Bel

iefs

Interactio

n

Know

ledge, Inform

ation, D

ataF

actsB

eliefs

TemporaryInternal Storage

In

Out

Has intended / expected meaningHas intended / expected value

TaskKID

TaskKID

Has temporal / spatial, pragmatic

components

KID

KID

KID

May be an Ontology/Semiotic

System

Receive KID Process KID Make Decisions Take Actions (provide a service)



Has role / set of responsibilities

Context

ContextContext

Interaction Interaction

Task KID In

Task KID Out

Di

DO

Di Di

DiDi

DO DO

DODO

System A System B

System C

General FlowEach (sub) system has role / set ofresponsibilities (e.g. in collaborations)Each (sub) system has a semantic state-space and is in a particular semantic state

C1

C2

Semantic interoperabilitybetween A and C-C1

Semantic interoperabilitybetween C-C2 and B

Semantic interoperability between C-C1 and C-C2

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The KID product and its’ quality attributes, produced as a service by System C at the request of and for System B, is a function of (with associated errors):

• Input data from System A (data supplier) and System B (data customer: KID requirements)– Task KID (has intended meaning)– Context KID (has intended meaning)

• The data capture mechanisms• Recognition processes (evaluation against a set of symbols, definitions, languages that are

known) physical / intrinsic attributes of data

• Level 1 Interpretation (by system) process (an Interpretation Function): KID in context– The selected / derived interpretation– Knowledge of context (e.g..... Pragmatics: spatial-temporal, linguistic)– Knowledge of KID in context (e.g..... expectation)From a data perspective Information defined as (data + Level 1 interpretation the data has

been attributed (extrinsic attribute) with meaning, as a function of context)From a knowledge perspective, this information is knowledge

• Evaluation of data + Level 1 interpretation against expectation (as a function of role for KID / KID requirements)

• Level 2 Interpretation process (an Interpretation function): System (interpreter) in context– The selected / derived interpretation– Knowledge of context (e.g..... Pragmatics: social, epistemic)– Knowledge of self in context (role, purpose, responsibilities, perspectives, focal points) –

significance of information to self and to super-systems – that data has been attributed (extrinsic attribute) with meaning.

– Knowledge (from data perspective) = information + level 2 interpretation (signification)• Evaluation / validation of interpretation against role

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• Data product production (action taken) and quality is a function of: (output data is attributed with an intended meaning)

– Interpretation of input data requirements, and purpose (where appropriate) from receiver (e.g..... customer)

– Context of the data production system– Knowledge of the context (Level 1 interpretation of input data)– Knowledge of self (role, etc.) (Level 2 interpretation of input data)– Decision making (e.g..... what KID to produce and how to produce it)

• Time (any of the above may change with time, e.g..... Learning)

• For successful semantic interoperability:– The attributed intended meaning by the sender (a service provider), at

level 1 and level 2 interpretation levels, must “match” the attributed level 1 and level 2 interpretations of the receiver (a service requestor)

• Due to the nature of the nature of meaningful exchange of KID, especially between machines, logic is required

• To ensure semantic interoperability, there must be measures.• It is noted that the level 1 and level 2 interpretations are extrinsic attributes of KID as opposed

to intrinsic (those attributes that are interaction context (including interpreter) independent. The quality of KID is a function of the intrinsic and extrinsic attributes.

• Successful interaction requires level 1 semantic interoperability, successful collaboration (common purpose / goals / objectives, etc.) requires level 2 semantic interoperability

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• For any group of systems to collaborate, to interact with a purpose, to meet goals and objectives, and produce data products as a group, it is required that there be a shared understanding of what the data resources mean

• The shared understanding may be created dynamically during the interaction – semantic negotiation, mediation, arbitration during a task with the use of an ontology system

• The shared understanding may be created a priori and is “static” before an interaction – a shared ontology or ontologies with mappings between them

• How does one know there is a shared understanding? - Measures


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Semantic Distance / Similarity and Matching


X = some KIDSupplier

InformationAnd

Management

CustomerInformation

And Management

X’1 = …..X’2 = …..X’3 = …..

E.g. Defense Logistics Agency – an Enterprise System

IndustrialService

Providers

-Information –- resources –

- money -

Customere.g.

War fighterSystem

X = (KID, intrinsic attributes, intended level 1 meaning, intended level 2 meaning)

X’ = (KID, intrinsic attributes, level 1 interpretation, level 2 interpretation)

A set of possible responsesBased on the set of interpretationsavailable (already known or derivable)

X = X’To what degree are these equivalent?Which alternative is best?Is there a match?

Note: Algorithms do not represent complete structures

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Semantic Distance / Similarity and Matching

X = ‘Customer’

Sender (in context)Set of possible interpretations

Receiver (in context)Set of possible interpretations

Set of possible interpretations in common

Interpretations are a function of context

•Each set of possible interpretations is constrained by the semantic state of each system.•Each semantic state is a function of context.•As contexts increasingly overlap, the semantic states overlap and the set of possible common interpretations increases. •The conditions / situation / constraints of the interaction reduces the possible number common of interpretations

A measure of semantic distance / similarity is a measure of the semantic difference between sets of concepts (e.g.... X and X’).A semantic match is when distance is zero.

SupplierSystems

Customer Systems

X = (KID, intrinsic attributes, intended level 1 meaning, intended level 2 meaning)

X’ = (KID, intrinsic attributes, level 1 interpretation, level 2 interpretation)

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s KID


• Example Scenarios – There may be no common interpretations – failure– There may a set of common interpretations but the semantic

distance between those of the sender (e.g.... marketing) and those of the receiver may be greater than some criteria – failure

– There may be set of interpretations of the receiver that are acceptable to receiver and sender – workable

– There may be a match - success• Interactions may occur many times with differing results as the

interpretations may vary – one form of semantic variance.• The sender (service provider, e.g. industrial enterprise) and

receiver (service requestor, e.g. supplier management system) interact with N and M other systems respectively. The same kinds of results occur with these interactions. There may be Z (sub) systems. Variance at the system (e.g. enterprise) level is some function of variance on individual interactions – enterprise semantic variance.

• There are many semantic measures that may be implemented to ensure successful collaboration – see appendix


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Characterization and Utilization of Ontology and Semantic Interoperability Systems to Address the

Semantic Interoperability Problem

Characterization and Utilization of Ontology and Semantic Interoperability Systems to Address the

Semantic Interoperability Problem



Relationships between semantic interoperability , ontology, information quality, service-oriented

architecture and (inter-) enterprise system architecture including reference models

Relationships between semantic interoperability , ontology, information quality, service-oriented

architecture and (inter-) enterprise system architecture including reference models


Ontology DefinitionA logical theory accounting for the intended meaning of a formal vocabulary of a domain, i.e. it’s ontological commitment to a particular conceptualization (a system of categories) of the world (N. Guarino, 1995) [underline, italics, and ( ) added]

– From a (general) systems theoretic perspective of the Meaning Triangle, an ontology represents a semiotic (semantic) system state, which in turn may be used to represent a domain of discourse of a set of external systems.

– Both sender and receiver agree on the meaning of a KID element. This agreed upon meaning is formalized and stored in an ontology. The intended meaning of a KID element sent from a sender is expected to be equivalent to the interpreted meaning of the KID element of the receiver.

– The semantic heterogeneity / semantic interoperability problem is an inter-system specification (build-time) and communication (run-time) problem and is addressed at the specification - interpretation point (e.g. between creating a definition and the interpretation of that definition).

– Sentences are built from the vocabulary. A set of sentences (facts) pertaining to a domain (of discourse) is a KB of that domain. This is the basis for ontology-based knowledge management and engineering.

– These facts are not fixed due to enterprise learning – open world semantics– An enterprise systems that is architected to include an ontology-based knowledge

management system with open world semantics provides the foundation for a disciplined approach to innovation

Semantic Interoperability and Ontology SystemsSemantic Interoperability and Ontology SystemsIn

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Semantic Interoperability and Ontology SystemsSemantic Interoperability and Ontology Systems

<X> SystemOperational

Administration and Monitoring

Tools

Stakeholder SystemsService Requester/

Service Provider

<X> System Services

<X> System APIs and Data Interchange

Product / ServiceDefinition / Specification

(Architects and Engineers)

Other <X> System Engines

(e.g. in a Federation)

<X> Systems<X> =

Ontology and Semiotic

<X> Interoperability-“the ability of two or more <X> engines to communicate and interoperate in order to coordinate and execute <X> service instances across those engines

May be remote

May be remote

<X> System Engine(“ a software service, or ‘engine’, that provides the run time execution environment for a <X>

System service instance”)

E.g. - real-time - monitoring

- analysis, prediction, optimization- self defining

- dynamic configuration- self managing, self-diagnosing, self-adapting

etc.

Modified from WfMC / OMB standardsModified from WfMC and OMG Standards

Interoperability

Ontologies

D

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Services orientation viaEnterprise architecturegovernment of SOA

A

BC



Sym

bols

-

Incl

udin

g te

rms

-

Log

ic L

angu

ages

O

WL,

Com

mon

Log

ic,

Def

easa

ble

Log

ic

Dom

ain

V

ocab

ula

ry-

Co

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lled

Voc

abu

larie

s -

- D

ictio

narie

s -

- T

hes

aur

i -D

efin

ition

s

Ontolo

giesLog

ical theories accounting

for the intended m

eaning of a fo

rmal (shared) vo

cabulary

Context

Context / Meaning BrokerMediator / Negotiator / Arbitrator

Rel

atio

ns

Meaning Triangulation™

Meta-ontologie

sLog

ical theories that unify (m

erge / m

ap)

other logical theories that account for the intende

dm

eaning of associate

d formal vocabularies

Ena

bles merging / m

apping be

tween ontolog

ies

Map

ping

s

Mea

sure

s

Expression Engine

(Met

a) L

angu

age

s (s

ynta

x)

Things referred to

Agents

Ontology and Semiotic System Engine Components

Reasoning Engine(including interpretation functions)Induction / Deduction / Abduction

COTS products

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OWL, ODM Standards



Has role / set of responsibilities

Context

ContextContext

Interaction Interaction

Di Di

DiDi

DO DO

DODO

System AIndustrialSuppliers

System BWar fighter

System

System CE.g. Defense Logistics

Agency

General Flow

C1

C2

Ontology (and Semiotic) Engine

AB B

C1-C2 Shared Ontology (and Semiotic) Engine

Ontology (and Semiotic) Engine

DD

E.g. SupplierSystems

E.g. CustomerSystems

Semantic (semiotic) systems layer supporting communication(s)

Meta DataRepository

COTS productsw/ standards

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May be a shared Ontology



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Sender Reciever

SenderReciever

System MSystem M’

Role N’Role N

“Instance” of an emergent Contract Negotiation SystemProduces a service to all parties involved in negotiations

The parties are part of a group


Data

ProcessData

Make Decisions

Take Actions

ReceiveData

ProcessData

Make Decisions

Take Actions

Contract NegotiationRequires Deontic / Modal Logic

Requires Semantic interoperability

Output is a contract documentContract has a role- contract is passive, role is assigned (role may be different among the parties)The contract has value-

Negotiation StylesCompetitive, Cooperation, Collaboration,Compromising, Accommodating, Avoiding

Each negotiation style results in some level of utility on both the individual basis and at the systematic level (ecosystem levels)

There is an efficiency associated with each style to reach a given level of utility

There are 2..N members / parties in a negotiation group

Agents may be aware of enteprise history, goals, strategies, current state including existing contractsà contract negotiated must be consistent with all of above

Rights, obligations, permissions,

consequences, duties, violations, powers

(authority)

Contract



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Sender Reciever

SenderReciever

System MSystem M’

Role N’Role N

“Instance” of an emergent / “programmed” Contract Fulfillment SystemProduces a service to all parties involved in the interaction

The parties are part of a group

The (business) services have value

Services are managedServices are governed. If a contract is in place, including rights, obligations, permissions, consequences, duties, violations, and powers (authority), then

the contract governs the interaction

Conduit / Channel / Media

ReceiveData

ProcessData

Make Decisions

Take Actions

ReceiveData

ProcessData

Make Decisions

Take Actions

Contract FulfillmentThe contract governs the interaction.Especially true with legal contracts-

Enterprise and Inter-Enterprise Architecture and Engineering

3 Examples Using Ontology Systems for Semantic Interoperability in Government

Enterprise and Inter-Enterprise Architecture and Engineering

3 Examples Using Ontology Systems for Semantic Interoperability in Government




Enterprise and Inter-Enterprise SystemsEnterprise and Inter-Enterprise Systems

Data “Certification”- a service -

(based on meta-data, federated schema, rules,

ontology)

Internet data via Process /

Workflow and Web services

Relational / Object Databases

May be federated

Workflow Datastore

“Federated” Data System

Data Ware-house

Single Source Legal Record

Real-time / ETL

Hybrid

Read-Only

Quality Check

Feedback

Feedback

Business Transaction

Data

Trans-modal input

Data flow is on a Trans-modal

“Enterprise Service Bus”Partial View

Records Datastore

Document Datastore

Copyright © 2005 Mathet Consulting, Inc.

Content Datastore

Ontology and Information Quality in Enterprise / Inter-

Enterprise Architecture Partial View

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Agency Distributed

LOB System

Agency Process / WF

System

Agency BRSystem

Agency DW System

Architecture /

Engineering

State Legislature

Law

AgencyPolicy, Planning and Legislative

Analysis

Federated and Local Ontology

AgencyPolicies

ProceduresDirections

Service

Agency KM System

Business Process

X

Business Process

Y

Business Process

X

IRS Regulations

SocialSecurity

StateAdminRules

AttorneyGeneral

Court Decisions

Agency Board

StateNew /

RevisedStatutes

MetaDataData Quality System

Summary TablesHybrid “ETL” / Real-

time SubsystemsAnnotated with

Ontology

Embedded With MDA

=


Ontology and Information Quality in

Enterprise / Inter-Enterprise Architecture

Partial View

BanksOther

Agencies

Money / Data

Economics OntologyLaw Ontology

Process OntologyRule Ontology

Etc.

A

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B

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All services (including ontology services)designed on top of unified and converged communications

Interoperable ontology engines


Core and Foundation Boundaries captured in service level

agreements and IIC

Disaster Recovry

Service Event Analytics Repository

(System(s) state as represented

by state of managed services / components)

Availability

Capacity and Performance Service Levels

(Agreements)

Continuity

Disaster Recovry

Architecture /Engineering

Quality

Service Event Repository(System(s) state as

represented by state of managed

Services / components)

Availability

Capacity and Performance

Service Levels (Agreements)

Continuity

Service Event Server / Services(Real-time Monitoring and

management of system(s) state)

Availability

Capacity and Performance

Service Levels Continuity

Disaster Recovry

Managed Component Sensors for: Availability Service LevelsCapacity / Performance Service LevelsContinuity Service LevelsDisaster Recovery Service Levels

Sensors are “embedded” in systems (components / services)

Each system may be considered to be a

component offering a service

Sets of collaborating systems

(components / services) may

function a a single service

System 2

System 1

System N

System 4

System 3

Service Event Analytics Server / Services

(Monitoring and management of system(s) state)

Descriptive Statistics:

Means, Variance,

Histograms,(Cross-)

Correlations,etc.

ANOVA / MANOVA

Econometric Time Series

SimulationPredictionOptimization

Reporting


Semiotics (Semantics)




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Summary and ConclusionsSummary and Conclusions

Enterprise and Inter-Enterprise Information Quality through Semantic Interoperability and Ontology Systems

Enterprise and Inter-Enterprise Information Quality through Semantic Interoperability and Ontology Systems


• Semantic interoperability – is pervasive and ubiquitous– between machines requires formal languages– requires objective measures based on measurement theory (a

model of a thing is required in addition to a definition)– is concerned with the differences in semantics between

interacting systems and the effects these differences have on interaction / collaboration

– is the driving force between a host of semantic measures that are useful for system-system interaction / collaboration (see glossary for partial list)

Summary and ConclusionsSummary and ConclusionsIn

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• Semantic interoperability, KID Quality and KID Quality Systems

– Formal models of quality are required

– Quality KID / measures need to be cast in terms of semantic interoperability just like any other KID / measures that take part in system-system interaction / collaboration

– Semantic interoperability issues exist between KID quality systems as well as between any other sets of interacting systems

– Formally defining what things mean enables formal data definitions.


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• Semantic Interoperability, KID Quality and KID Quality Systems, and Ontology (and Semiotics) Systems

– As logical theories accounting for the intended meaning of a formal vocabulary, ontologies enable formal quality measures, including semantic measures to be defined and applied (meanings are formalized)

– Ontology systems provide tools to engineer new ontologies– Ontology systems provide a semantic reference point for semantic

interoperability between interacting / collaborating systems – conformance to an ontology.

– Ontology Systems stores background KID associated with the production of new KID

– A Quality Ontology may be constructed from quality standards and best practices, including CMMs, Continuous Process Improvement, ISO, QA, QC, Independent Validation and Verification, TQ(d)M, Six-Sigma

– An example of an Ontology language is OWL• Ontologies provide a vocabulary and definitions of rules for use by independently

developed resources, processes, services, systems, etc.• Ontologies enable agreements, e.g..... legal contracts, service-level agreements, to

be made among organizations sharing common services with regard to usage and meaning of relevant concepts that can be expressed unambiguously

• Independently developed systems, agents and services can work together to share information and processes consistently, accurately and completely by composing component ontologies, mapping ontologies to one another and / or mediating terminology among participating resources and services, Ontologies also facilitate conversations among agents to collect, process, fuse, and exchange information.

• Ontology (and Semiotic) Systems provide a common facility of semantic lifecycle mechanisms.


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• Enterprise and Inter-enterprise Systems Interaction and Collaboration– Ontology systems enable semantic interoperability

between enterprises where interacting enterprises have minimal shared context

– Interoperable ontology systems provide a semantic systems layer for semantic interoperability between interacting / collaborating systems

– Architectural Styles for addressing semantic interoperability include: brokered / mediated, common / merged, distinct / mapped

– Introduction of ontology / semiotic systems into enterprise systems elevates the enterprise to a higher systems capability level – symbol processing.


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• Semantic Alignment• Semantic Coupling• Semantic Cohesion• Semantic Conflict

Semantic Defect• Semantic Dependability• Semantic Difference• Semantic Discrepancy• Semantic Distance• Semantic Divergence• Semantic Drift • Semantic Error • Semantic Exception• Semantic Fault• Semantic Failure• Semantic Heterogeneity• Semantic Incident • Semantic Incident Prevention• Semantic Incident Detection• Semantic Incident Response• Semantic Incident Review

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sAppendixAppendix


• Semantic integration• Semantic Interoperability• Semantic Interoperability necessary and sufficient conditions• Semantic Issue• Semantic Match / Mismatch• Semantic Quality of Service• Semantic Problem• Semantic Proximity• Semantic Reliability• Semantic Risk• Semantic Service Levels • Semantic Stability• Semantic Stationarity• Semantic Similarity• Semantic Shift

Semantic Reliability• Semantic Robustness• Semantic Tolerance• Semantic Variance / Variability• Semantics• Semiotics• System Interoperability• System Failure • System Fault

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sAppendixAppendix


• Systems, S, of Interest

– A set of atomic symbols (e.g..... terms), which may be composed. For example, .A set of definitions (e.g..... connotations, denotations)

– A set of conceptual things – concepts / conceptual (sub) systems– A set of real-world things (subsystems), a subset of which are active– A set of relations between / among symbols, definitions, concepts, real-world things– A set of mappings between / among symbols, definitions, concepts, real-world things– A set of languages used to express symbols, definitions, concepts, real-world things,

relations and mappings– A set of allowable “configurations” / states between / among symbols, definitions,

concepts and real-world things constrained by the mappings / relations expressed with the languages

– A set of disallowed “configurations” / states between / among symbols, definitions, concepts and real-world things constrained by the mappings / relations expressed with the languages

– A set of relations between real-world / concept things (subsystems) and allowable system states, and disallowed system states

– A set of mapping functions between real-world / concept things (subsystems) and allowable, and disallowed system states

– A set of (sub) system boundaries, any one of which may be opened/closed, to some degree (e.g..... some set of inclusion / exclusion functions that operate on the above sets)

– (Sub) systems may be nested to some N levels– The whole is greater than the sum of the parts – new structure / behavior emerges from

interaction between the parts– Time – any of the above may change with time (i.e. is dynamic ; e.g..... learning,

adaptability, etc.)

Semantic InteroperabilitySemantic InteroperabilityIn

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Contact InformationContact Information

Mathet Consulting, Inc.Integrated, Interoperable and Collaborative Systems

MC

‘PMB 140041450 E. American LaneSchaumburg, IL 60173Office: 847-330-6375Cell: [email protected]

mailto:[email protected]

http://www.mathet.com/

integration, interoperability and collaboration: semantic interoperability and ontology systems...

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contextgeneral systems