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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
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
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 
3 
Does it only change the engineering 
process or also the design itself?
Knowledge Maturing Model: 
How knowledge develops 
4
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 
5
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 
6
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 
7
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. 
8
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 
9
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
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 
11
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. 
12
Example People Tagging: SOBOLEO 
13 http://soboleo.knowledge-maturing.com
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 
14
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 
15
Example: LivingDocuments (2) 
16
Summary 
17 
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
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! 
18
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

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Designing for knowledge maturing: from knowledge driven software to supporting the facilitation of knowledge development

  • 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 3 Does it only change the engineering process or also the design itself?
  • 4. Knowledge Maturing Model: How knowledge develops 4
  • 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 5
  • 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 6
  • 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 7
  • 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. 8
  • 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 9
  • 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 11
  • 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. 12
  • 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 14
  • 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 15
  • 17. Summary 17 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! 18
  • 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