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

Living Ontologies: with applications to Business Process Alignment and Building Consensus. Peter Weinstein, PhD Altarum Institute March 28, 2006. Living Ontologies. A way to use ontologies designed to evolve Ongoing opposing processes

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

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  1. Living Ontologies:with applications to Business Process Alignment andBuilding Consensus Peter Weinstein, PhD Altarum Institute March 28, 2006

  2. Living 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 Similarities Core Concepts Generic Concepts Organization-Specific Concepts Core Concepts Organization-Specific Concepts Differences Original Models Unified Model Model unification creates a middle layer of shared concepts

  3. Unification Algorithm • A swarm intelligence approach • Concept agents seek matches that maximize similarity • Based on lexical association and structural isomorphism “Musical chairs”: when a concept moves it often kicks another out of its match

  4. Problem 1 – Business Process Alignment • Want to analyze business processes for interoperability or reengineering, but … • Semantic heterogeneity impedes comparison Business process models can be hard to compare

  5. Solution Overview -Business 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 Process Users interact with problem-specific models such as process flow diagrams Flow Swim lane

  6. Comparison of Unified Models • Visualization of similarities and differences • Quantification of process alignment in [0, 1] pink = similaritiesblue/green = differences A comparison visualization of manually unified models

  7. Initial 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 Automatic unification finds more commonality than exists in manually unified model

  8. Problem 2 – Political 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

  9. Speaking To Consensus Builder User helps system interpret their statement

  10. Listening in Consensus Builder Compare statements to mediate exchange

  11. Be Counted A tool for learning

  12. Conclusions • 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

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