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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
Citation preview
http://knowledge-maturing.com
I-KNOW 2014, Graz, Austria
Designing for knowledge maturing: from knowledge-driven software to supporting the facilitation of knowledge development
Andreas P. SchmidtKarlsruhe University of Applied Sciences
Christine KunzmannPontydysgu Ltd.
http://employid.euhttp://learning-layers.eu
Trends in software engineering
Making software engineering more responsive to change Agile software development, continuous delivery
Making complexity of domains more manageable Knowledge-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
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But what about agility for
knowledge-driven applications?
But what about agility for
knowledge-driven applications?
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?Does it only change the
engineering process or also the
design itself?
Knowledge Maturing Model:How knowledge develops
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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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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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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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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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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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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.
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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
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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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Example People Tagging: SOBOLEO
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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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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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Example: LivingDocuments (2)
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Summary
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TypePoint in time
Roles Processes Implications
Hardcoded knowledge
design timedesigner/ developer
(software engineering)
-
Descriptive knowledge representation
design time / runtime
adminhardcoded (for admin)
separation of knowledge and other components
Participatory evolution of knowledge representations
runtime userhardcoded (for users)
knowledge representation formalisms understandable for end users; support for user contributions
Self-Organized knowledge modeling processes
runtime user socially negotiatedsupport for activities instead of processes; negotiation spaces
Facilitated knowledge processes
runtimeuser + 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
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Engineering and using
software is a
knowledge maturing
process!
Engineering and using
software is a
knowledge maturing
process!
Contact
Christine KunzmannPontydysgu Ltd.Ankerstr. 4775203 Königsbach-SteinTel: +49-7232-4093309mail: [email protected]://christine-kunzmann.de
Andreas P. Schmidt Karlsruhe University of Applied SciencesInstitute for Learning & Innovation in NetworksMoltkestr. 30 76133 Karlsruhephone: +49 (0)721 925-2914mail: [email protected]://andreas.schmidt.name
http://knowledge-maturing.com
