Contributed on October 24, 2012

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Tagging for Process Models in Knowledge Management

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Semantic Integration of Process Models into Knowledge Management: A Social Tagging Approach Michael Prilla Information and Technology Management (IMTM) Institute für Applied Work Science (IAW) Ruhr University of Bochum IMTM – An interdisciplinary Team: Applied Work Science, Communication Science, Computer Science , Engineering Science, Pedagogy, Sociology, Social Science Focus areas: Process Modeling Knowledge Management Innovation and Creativity Initial Situation

• Process Models are useful…

• Essential tools in business and science

• Encoding of organizational knowledge

• …but neglected in Knowledge Management

• KM dominated by textual content

• Limited access to available (PM) knowledge

• Lacking relations to other content

• Available support by isolated applications

Resulting Problems

• Observations

• Scarce usage in Organizations

• Lacking support for Work with Process Models (Knowledge Work)

• Solution: Integration into KM

• Promoting PMs as valuable information sources

• Supporting model related tasks

• Approach

• Social Tagging and Integration into Work Tasks

• Work In Progress

Fostering Usage of Process Models Scarce knowledge of modeling Scarce Usage and Awareness of Models Improve awareness of process models as information source: Integrate Process Models into KM content Improve modeling skills: Teaching and Training BUT: Cold start problem Still lacking awareness Fostering Usage of Process Models Increasing knowledge of modeling Usage and Awareness of Models Improve awareness of process models as information source: Integrate Process Models into KM content Motivation to create and share models Support for modeling and knowledge acquisition Analysis: Requirements

• Overcoming Complexity Gap: Semantic Description

• Low usage burden (high ceiling)

• Motivation for participation

• Integration of Stakeholders (KWs)

• Give everybody a voice

• Improve knowledge acquisition and distribution

• Integration into Work Tasks

The Complexity Gap

• Linear and coherent structure

• Overview difficult (on first sight)

• Understanding needs subjection

• Ramifications and sub-procedures

• Quick overview (scanning)

• Understanding needs context

Overcoming the Gap in KM needs equal handling: Semantic Content Description Social Tagging for equal Handling

• Semantics to level out the Gap between heterogeneous content

• Analysis of available Technology

• Conclusion: (Starting from) Tagging suitable for task at hand

Requirement Social Tagging Ontologies Semantic Content Description + * ++ Low Usage Burden (and high Ceiling) ++ (learning effort, quality) o (adoption and adaptation) Stakeholder Integration ++ (bottom-up, associative) o (learning effort) Tagging für Process Models Existing Tags slides present presenting finish talk powerpoint instruction nervous questions presentation Supporting Knowledge Work with PMs

• Limited support  limited content distribution and access

• Support for Knowledge Workers

• Freedom of choice for Tools

• Personal Information Management

• Implications

• Integrate support into available tools

• Embed group-related tasks into individual work

• Intertwine models and KM content

Implementation

• Supporting functions:

• Sharing from modeling applications

• Intertwining of textual and model content

• KM support for modeling task

• Prototype

• Kolumbus 2 KM application

• SeeMe modeling editor

Share and Intertwine with KM content Share models, tags and related information from modeling applications Intertwine models and other (existing) content by tags Support modeling task Deliver related information (text, models, …) while modeling Summary and Further Work

• Integration of Process Models into KM content

• Bottom-Up, Social Tagging

• Tight integration into Knowledge Work

• Remaining Work

• Continuation of Empirical Grounding

• Refinement of Prototypes

• Improvement of Tagging support (Clustering, …)

• Evaluation

• Integration of other Applications

Semantic Integration of Process Models into Knowledge Management: A Social Tagging Approach Michael Prilla KM-supported Model Lifecycle

• Support exchange:

• Integrate in KM content

• Contextualize

• Include corresponding information

Integration into Modeling KM with Process Model Support

• Support usage:

• Make use of encoded knowledge

• Reach relevant actors

• Support enrichment:

• Expatiate actors’ perspectives

• Add context

• Link content

• Support modelling:

• Show contextualized information

• Offer similar models

Small user groups Implementation

• Enable tagging support for applications

• Tags provide (semantic) glue for content linkage

• Prototype: SeeMe Model Editor and Kolumbus 2

Technical support: Framework

• Receive content while modelling

• Tag based queries

• Stream content

Tag Based Exchange via Web Service

• Share models from modelling application

• Basis: Tags

• XML exchange

• Include linked content

Generic framework: Integrate multiple application s and information sources Synergy potential Link relevant knowledge to BPM content (filter) Process oriented KM (Semantic) Business Process Management Collaborative process modelling tools Knowledge Management for Process Models Use data from BPM in KM system Find related processes Provide helpful data while modeling (Semi-) automatically add content to structure Share perspectives Semantics: Collaboration vs. Publishing

• Control by a few

• Anticipation of categorization

• Unidirectional information exchange

• Slowly changing

• Useful in slowly changing, precise information structures and vocabularies

Social Tagging: Collaboration Content Content Categories, Ontologies: Publishing

• Control by all Users („Prosumers“)

• Emergence of categorization

• Bidirectional information exchange

• Flexibility

• Useful in quickly adapting, multi-perspective and fuzzy information structures

Semantic Spectrum

• Choice of semantic support depends on

• Task to be supported and overall goal

• Organizational culture (hierarchies, …)

• Present vocabulary

• People working with it

Ontologies Taxonomies Thesauri Categories Tag Clusters Tag Proposals Free Tagging Flexible Perceptable Ambigious Individual Easy Flat Precise Deductive Machine readable Levelling Complex Fixed Model contextualization business order management Users‘ perspective on content Source: Shaw and Gaines (1998) Furnas, CHI 2006: “different users use different terms to describe the same thing” Domain User 2 User 1 User 5 User 3 User 4 Terminology Terminology Terminology Terminology Terminology