Living Ontologies: with applications to Business Process Alignment and Building Consensus Peter...

Living Ontologies:Living Ontologies:

with applications to with applications to Business Process Alignment andBusiness Process Alignment and

Building ConsensusBuilding Consensus

Peter Weinstein, PhD

Altarum Institute

March 28, 2006

2www.altarum.org AAAI SSS 06 - Semantic Web meets eGovernment

Living OntologiesLiving Ontologies

A way to use ontologies designed to evolve Ongoing opposing processes

– Differentiation: Users specialize terms for model accuracy– Unification: Identify commonality with graph matching

Model unification creates a middle layer of shared concepts

Similarities

Differences

Core Concepts

Organization-Specific Concepts

Core Concepts

Generic Concepts

Organization-Specific

Concepts

Original Models Unified Model


Unification AlgorithmUnification Algorithm

“Musical chairs”: when a concept moves it often kicks another out of its match

A swarm intelligence approach Concept agents seek matches that maximize similarity

– Based on lexical association and structural isomorphism


Problem 1 Problem 1 – – Business Process AlignmentBusiness Process Alignment

Want to analyze business processes for interoperability or reengineering, but …

Semantic heterogeneity impedes comparison

Business process models can be hard to compare


Solution Overview -Solution Overview -Business Process AlignmentBusiness Process Alignment

Model processes on two levels– Users work with familiar diagrams and other tools– Internal representation with formal ontology

Unify the models– An automatic process assisted by users (anytime, anywhere)

Compare processes

Users interact with problem-specific models such as process flow diagrams

Process

FlowSwim lane


Comparison of Unified ModelsComparison of Unified Models

A comparison visualization of manually unified models

pink = similaritiesblue/green = differences

Visualization of similarities and differences Quantification of process alignment in [0, 1]


Initial ResultsInitial Results

Experimental data– Four purchasing processes for medium-sized manufacturers

Compared automatic to manual unification– Current automatic results are “too good”– Next step: richer multi-level data

Commonality Identified

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

3500

7000

1050

0

1400

0

1750

0

2100

0

2450

0

2800

0

Process Step

Per Match Avg. Portion Properties

Shared

Manual baseline

0% confirmed

30% confirmed

Automatic unification finds more commonality than exists in manually unified model


Problem 2 Problem 2 – – Political DiscoursePolitical Discourse

Consensus Builder will be a place on the internet where people go to:

–Speak about things they know and care about

–Listen to others (if or when they are ready to listen)

–Be counted by a system that aggregates and

publishes beliefs


Speaking To Consensus BuilderSpeaking To Consensus Builder

User helps system interpret their

statement


Listening in Consensus BuilderListening in Consensus Builder

Compare statements to mediate exchange


Be CountedBe Counted

A tool for learning


ConclusionsConclusions

Living Ontologies evolve through use– Tolerate differences, maximize similarity– Wrap agents around concepts to self-organize

Applications meet users where they work– Ontologies belong under the hood

Benefits can include– New scientific rigor for Business Process

Reengineering– Knowledge sharing to facilitate political discourse

Living Ontologies: with applications to Business Process Alignment and Building Consensus Peter...

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