Software engineering has been transformed in recent years by understanding the interaction with customers and the target context as an ongoing learning process. Responsiveness to change and user-centered design have been the consequences. In a similar way, knowledge and ontology engineering are undergoing fundamental changes to acknowledge the fact that they are part of a collective knowledge maturing process. We explore three examples: (i) social media based competence management in career guidance, (ii) ontology-centered reflection in multi-professional environments in palliative care, and (iii) aligning individual mindlines in pratice networks of General Practitioners. Based on these, we extract four levels of designing for knowledge maturing and associated technical implementations. This shows that future technology support should especially target facilitation of self-organized, but tool-mediated knowledge development processes, where, e.g., workplace learning analytics can play a prominent role

1. Designing for knowledge maturing:
from knowledge-driven software to
supporting the facilitation of
knowledge development
I-KNOW 2014, Graz, Austria
http://knowledge-maturing.com
Andreas P. Schmidt
Karlsruhe University of Applied Sciences
Christine Kunzmann
Pontydysgu Ltd.
http://employid.eu
http://learning-layers.eu

2. Trends in software engineering
 Making software engineering more responsive to change
ÞAgile software development, continuous delivery
 Making complexity of domains more manageable
ÞKnowledge-driven applications, semantic technologies
 Software engineering is a mutual learning process of
designers and users in which designing tools deepens the
understanding of the domain
2
knowledge-driven
what about agility applications?
for But

3. Background: Where we are
 Classic knowledge engineering methods are inspired by
waterfall-like models
 Emphasized strict phases and the formalization step
 Neglected the complexity of social processes that
construct a shared understanding on an ongoing basis
 Recent developments in the direction of „continuous
knowledge engineering“
 mostly based on the Wiki paradigm
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Does it only change the engineering
process or also the design itself?

4. Knowledge Maturing Model:
How knowledge develops
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5. Knowledge Maturing & Design Processes
 Design process itself is a knowledge maturing process in
which the knowledge how to support a domain and its
users in the best way develops
 Knowledge maturing distinguishes between the
(collective) knowledge and the artifacts used to represent
 Co-existence of different levels of maturity and formality
 Most knowledge engineering methodologies have so far
focused on phase IV and phase V, some addressed phase
III, neglecting the early phases
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6. Typology of knowledge-based applications
 We are using a typology to illustrate the impact this
maturing process has on the design
 Design time vs. runtime
 When does knowledge become part of the application?
 Roles for developing knowledge
 Who develops knowledge? Who evolves the
representations in the application?
 Processes for developing knowledge
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7. I. Hardcoded Knowledge
 During the requirements phase, domain knowledge is
collected by business analysts, modelled in an appropriate
way (UML & Co.) and passed on to developers
 Knowledge becomes implicit in the code
 Weaknesses:
 Responsiveness to change:
• Requires long release cycles
• cannot deal with fast-moving domains
 Knowledge ready at design-time:
• Basic assumption that knowledge can be „collected“ at design
time is fundamentally flawed: it needs to be co-constructed
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8. 2. Descriptive Knowledge Representation
 Separate algorithms from descriptive knowledge
 Long history in computer science, especially in AI
 Two approaches
 Engineering approaches: humans create the models
 Mining approaches: algorithms create the models
• But co-construction required from a KM-perspective
• Therefore human-understandable descriptive models
 Advantages:
 Knowledge representations can become the focus of
reflection
 Functional framework can be applied to multiple domains as
domain knowledge can be exchanged.
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9. 3. Participatory evolution
of knowledge representations
 Problem:
 Large time lag between need arising and actual change
 Motivational issues, low rates of feedback, barriers to
negotiation processes
 Increase participation through social-media inspired
approaches
 From controlled vocabularies to tagging
 Wiki-based modelling of domain knowledge
 Knowledge modeling becomes a runtime activity
 From expert-based modelling to broader range of
participants
 Impact on suitable formalism
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10. Example: SpirOnto
 Improving spiritual care in
a multi-disciplinary setting
 Annotation of patient-care
records with an ontology
to cross-link cases and
reflect on insights
 Links observations to
concepts and possible
interventions
 Ontology can be amended
by users and is subject to
empirical research.
10
http://spironto.de

11. 4. Self-organized
knowledge modelling processes
 Problem:
 Even if knowledge modelling has become a runtime
activity, the rules and processes to regulate contributions
are still part of tool design
 But especially social media has shown: appropriation as
actual use differs from intended use so that built-in
regulations come into the way
 Therefore: socially negotiated processes:
 Gardening
 Implications:
 Tools don‘t provide processes, but support activities
 Processes are negotiated by users
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12. Example: People Tagging
 Social media approach to competence management
 Supports a self-organized ontology maturing process
 People can be tagged, but the system suggests tags
 Users can merge and hierarchically structure tags
 Results in a SKOS ontology
 Some users assume responsibility for gardening tasks
although no formal role is prescribed.
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13. Example People Tagging: SOBOLEO
13 http://soboleo.knowledge-maturing.com

14. 5. Facilitated knowledge processes
 Problem: Self-organized processes are a challenge for
users, increasing complexity
 We have only focussed on users, not on helping users
 Facilitation
 Human facilitation
 Facilitation through tool functionality
 Facilitation through environments
 Functionality
 Recommendations, triggers
 Negotiation spaces
 Reflection, analytics
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15. Example: LivingDocuments
 Facilitation by overcoming social barriers of lack of
confidence to deal with sharing knowledge in early
phases
 LivingDocuments provides a collaborative editing
environment and concentrates on supporting the
negotation processes
 Currently focused on semi-structured documents
 But principle could be extended to more formalized
artefacts
 Facilitating the negotiation process by two key aspects
 Indicate maturity of contributions
 Maturity-aware creation of awareness about changes
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16. Example: LivingDocuments (2)
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17. Summary
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Type Point in time Roles Processes Implications
Hardcoded knowledge design time
designer/
developer
(software engineering) -
Descriptive knowledge
representation
design time /
runtime
admin hardcoded (for admin)
separation of knowledge and
other components
Participatory
evolution of
knowledge
representations
runtime user hardcoded (for users)
knowledge representation
formalisms understandable for
end users; support for user
contributions
Self-Organized
knowledge modeling
processes
runtime user socially negotiated
support for activities instead of
processes; negotiation spaces
Facilitated knowledge
processes runtime
user +
facilitator
socially negotiated with
facilitation support
support for facilitating roles
and activities

18. Conclusions
 Do not hardcode knowledge into designs –
make software knowledge-driven
 Tear down the wall between design time and runtime -
knowledge models can be changed by users
 Let users define their social processes for developing
knowledge models - support activities, not processes
 Support facilitators in this process through analytics:
support guidance activities
Engineering and using
software is a knowledge
maturing process!
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19. Contact
Christine Kunzmann
Pontydysgu Ltd.
Ankerstr. 47
75203 Königsbach-Stein
Tel: +49-7232-4093309
mail: kontakt@christine-kunzmann.de
http://christine-kunzmann.de
Andreas P. Schmidt
Karlsruhe University of Applied Sciences
Institute for Learning & Innovation in Networks
Moltkestr. 30
76133 Karlsruhe
phone: +49 (0)721 925-2914
mail: andreas_peter.schmidt@hs-karlsruhe.de
http://andreas.schmidt.name
http://knowledge-maturing.com