Methodologies for Active Knowledge Architectures

April 23, 2009

Innovative design is the most important competitive factor for global engineering and manufacturing. Critical challenges include cutting lead times for new products, increasing stakeholder involvement, facilitating life-cycle knowledge sharing, service provisioning, and support. Current IT solutions for product lifecycle management fail to meet these challenges because they are built to perform routine information processing, rather than support agile creative work.

Active knowledge modeling is a family of methodologies that address this situation, utilizing model-driven application platforms. This post presents an overview of different methodologies applied to implement pragmatic and powerful design platforms, by building and utilizing active knowledge architectures (AKA).

A previous post introduced the basic concepts of knowledge architectures. Here we outline the different knowledge spaces that such architectures consist of, and the core knowledge dimensions in each space. Future posts will describe each methodology in more detail. The collaborative product and process design methodology describes how teams should work together in innovative design spaces. How to configure an AKA platform to support such teams with model-configured workplaces for the different roles, is described in the visual solutions development methodology. Further methodologies yet to be developed should extent the approach into the knowledge spaces for business management, networked enterprises, and industrial community development.

Knowledge Spaces

A knowledge space is a four-dimensional representation, where the dimensions are mutually reflective, capable of altering each others’ structures, content, and meaning. AKM methodologies are built upon a common framework, called the Enterprise Knowledge Architecture (EKA). The EKA defines the dimensions of four nested knowledge spaces:

  • The personal workspace, reflecting a user’s work and knowledge so that the information system can adapt to it, as information content, roles, tasks and views (IRTV).
  • The innovation space, reflecting the products, organization, processes and systems (POPS) of an interdisciplinary team collaborating, e.g. in product design.
  • The business networking space, reflecting how companies come together in value net-works and supply chains, their services, networks, projects and platforms (SNPP).
  • The community space, reflecting how larger industries, sectors, cultures, and societies function, their values, resources, initiatives and infrastructures (VRII).

There are methodologies associated with each of these knowledge spaces. Below, we first introduce the basic dimensions of each space, and then we outline the principles for how the knowledge spaces are integrated into a holistic knowledge architecture.

Personal Workspace

A personal workspace should contain everything that someone needs for performing their work. In order to reflect this space, we need to model the four dimensions depicted below:

  • Information (I), which information is needed to perform the work, which information is produced, etc.
  • Roles (R), who are involved in the work, what is their responsibilities, which tasks do they perform, which information do they use, which views should their workplace consist of etc.
  • Tasks (T), which task are performed, which services are used to achieve the results.
  • Views (V), which views should be available in the workplaces, which information and services should they give access to, what should it look like, etc.
Dimensions of personal knowledge spaces

Dimensions of personal knowledge spaces

As shown, the dimensions are mutually dependent. Tasks require and produce information, tasks are performed by roles, roles are defined by the tasks the role is responsible for, roles need access to information, information is owned by roles, views are applied by roles performing tasks on some information, etc. Understanding and managing these dependencies are crucial for designing the right information, role, task and view models. The four dimensions should thus be designed together, following the Visual Solution Development Methodology. IRTV models typically contain several relationships between the elements in each dimension (information models, task patterns etc.), and as well between the dimensions (e.g. roles and tasks such as a UML use case diagram). Large hierarchies of elements are however less common in this layer. Instead, task are often organized into process hierarchies, information by product structures, roles into organizational structures and views into application systems, in the surrounding innovation space discussed below.

Innovation Space

The innovation space defines the core structures of teamwork, especially in design projects. It contains these dimensions:

  • Product (P), the result and content of the work,
  • Organization (O), the personnel resources and skills required, available or applied,
  • Process (P), the structure of work tasks,
  • System (S), the underlying support tools and equipment used.
Dimensions of team and project knowledge spaces

Dimensions of team and project knowledge spaces

Business Networking Space

Behind the creative work performed in innovation spaces, we find strategic management and business transactions, establishing networks of groups and companies working together in value and supply networks, markets, and consortia. The dimensions of this space are:

  • Services (S) required and provided by the different companies and groups,
  • Network organization (N) structures of established collaborations,
  • Projects (P) where multiple partners cooperate to create new services, and
  • Platforms (P) providing interoperable IT support for the networks.
Dimensions of business knowledge spaces

Dimensions of business knowledge spaces

Industrial Community Space

Finally, the backbone of personal knowledge spaces, innovative teams, businesses and networks, is the society, culture, industrial setting etc. where the businesses operate. Though not under the control of the business, they influence the operation of most businesses profoundly, in a number of dimensions:

  • Values (V) represent the worth of commodities, services, assets, or work, and the principles, standards, or quality which guides human actions.
  • Resources (R) are the personnel and material applied to create value.
  • Initiatives (I) to apply resources and infrastructure to create new values.
  • Infrastructure (I) is the overall set of tools and mechanisms for communication, logistics, and value creation in general.
Dimensions of industrial community knowledge spaces

Dimensions of industrial community knowledge spaces

Future AKM-based corporate governance and community building methodologies need to assess value propositions, resource development and management, initiative portfolio management, infrastructure extension and maintenance. In a design project, a process is followed by an organization, using a system to develop a product. Again, the dimensions are mutually dependent on each other, and should be designed together, using the collaborative product and process design methodology.


The table below summarizes the main knowledge spaces discussed above, illustrating as well how the dimensions reflect what to produce, who should do the work, how they sho go about, and the tools applied.

  What/Why Who How Enabler
Community and network Value Resource Initiative Infrastructure
Business Service Network Project Platform
Project and team innovation Product Organization Process System
Individual Information Role Task View
Software Data User Code Programming

Integrating Knowledge Spaces in a Knowledge Architecture

Perhaps even more important than the reflection inherent in a knowledge space, is reflection between the knowledge spaces, e.g.:

  • How the IRTV models are reflected into software services to create customized IT solutions, with parameterized services and information content available through role-specific workplaces.
  • How innovation space models, such as the collaborative product design methodology, map to IRTV models for configurable IT support
  • How IRTV models capture bottom-up tasks, information content and targeted roles that need to be managed through the hierarchical process, product, organization and system structures.

As depicted below, the dominant relationships for managing lower layer structures typically follow the main dimensions. For instance, tasks are aggregated into processes, roles into organizations, views into systems, and the primary information elements reflect product structures.

Major dependencies between project team and personal knowledge spaces

Major dependencies between project team and personal knowledge spaces

This simplistic view is held by most modeling methodologies, such a business process management, information and data modeling, organization hierarchy charts etc. It does however violate one of the core principles of knowledge spaces, that the dimensional views are all mutually reflective and interdependent. It’s single-dimensional, top-down perspective also violates common sense: Of course, processes, organizations and systems are described as information as well, not just products. In the design, the product structures capture the critical dependencies between tasks, and the product structures are also used for managing and coordinating tasks, e.g. monitoring the progress of product component design rather than the progress of subprocesses and subtasks. Similarly, administrative tasks such as reporting emerge from relationships in the organization structure, and low level tasks are associated to the usage of information systems. Likewise, roles deal with responsibilities towards processes, products, and systems, not just the simple organizational structures. The figure below thus gives a more accurate, albeit complex representation of how the innovation space is reflected into personal workspaces, and vice versa.

All dependencies between project team and personal knowledge spaces

All dependencies between project team and personal knowledge spaces

The figure above also shows that each of the POPS elements can be seen from four different workspace perspectives (sides), as information, role/responsibility, task, and view, respectively. When these spaces are brought together, we thus define derived concepts illustrated by the above relationships, such as

  • product role, process role, organization role, and system roles
  • product, organization, process, and system information product lifecycle,
  • organizational, process, and system tasks
  • product, organization role, process phase, and system (aspect) view.

Similar arguments can be made for the surrounding business and community knowledge spaces. Knowledge represented in these spaces may be reflected through the innovation space dimensions to be made more concrete, e.g. for

  • designing values and services as products,
  • linking networks and community resources to organizational structures,
  • detailing the processes that implement projects and initiatives, and
  • the systems that infrastructures and platforms consist of.

Business and community knowledge may however also be directly reflected through the personal workspace dimensions (IRTV) to create operational solutions without worrying about the details of particular design and innovation settings.

Future posts will describe the visual solutions development and collaborative product and process design methodologies.


6 Responses to “Methodologies for Active Knowledge Architectures”

  1. […] processing and management, and research intensified on corporate memories. One may regard an Active Knowledge Architecture (AKA) as a visually operated corporate memory. Most conferences in those days discussed the core […]

  2. […] cooperation with industrial companies we have built pilots to demonstrate the capabilities of Active Knowledge Architectures. Pilots validate the ability of the approach to meet customer expectations, secure involvement, […]

  3. […] all levels of interoperability, not just in the organizational and political contexts. Such a multi-dimensional approach is at the core of […]

  4. Hossen shokohi Says:

    Dear Sir
    Harvard Jorgensen
    My name is Hossein Shokohi. As a Ph.D student in Curriculum Studies, I am studying on my thesis about Knowledge Architecture. I have some questions:
    1. What are the components of Knowledge Architecture?
    2. What is the difference between Knowledge Architecture and Knowledge Management?
    3. Is there any questionnaire on Knowledge Architecture?
    Please also introduce some notable references for helping me in these questions.
    Yours faithfully
    H. Shokohi

    • Dear Hossen Shokohi.

      I’m afraid I don’t know much about curriculum studies, so I apologise in advance if my reply does not fully answer your questions.

      ‘Knowledge architecture’ as we use the term, is really a paradox, and I am not completely comfortable about the choice of words. We disagree with the definition of knowledge commonly applied in artificial intelligence, knowledge representation, knowledge based systems, expert systems, knowledge based engineering, semantic technologies and related disciplines. These disciplines see knowledge as something that can be encoded in computers, using formal logic and mathematical languages, as something that can be studied independently of the human mind. We see knowledge solely as a property of the mind, any external representation is just data, requiring a human being to be interpreted (into information). Information may influence actions and cause learning, in which case it may be observed as knowledge. Formal mathematical languages represents a step away from knowledge, because they are difficult to read and interpret for most human beings. They are designed for machine processing. In this sense, natural language is a far better form of knowledge representation, and even programming languages are a lot better than formal languages. This is why logic programming languages never could compete with block-structured and object oriented ones.

      The history of computing can be seen as a development of the language of human-machine-interaction towards higher levels of abstraction, away from computer languages and closer to human languages, e.g. from machine code and assembly to third and fourth generation programming languages. We see visual languages as the next step in this development, because they offer a unique combination of being understandable and useable for most people, while at the same time being structured and more suitable for computer processing than e.g. natural language. At the same time, when looking for ideas on how to design visual languages, we find it far more important to study how human beings create and use data, information and knowledge, than to look at how computers do it. We are trying to move closer to the users, after all. In this perspective, ‘knowledge architecture’ means an approach to visual modeling that focuses more on the users and less on tha algorithms. This is contrary to e.g.’model driven architecture’, which basically define visual representations of programming languages. UML is a typical case.

      Of the social science work that we’ve found particularly useful, I would like to recommend Donals Schon’s “The Reflective Practitioner: How Professionals Think In Action” on the level of individuals, and Etienne Wenger’s “Communities of Practice: Learning, Meaning, and Identity” on the social level. On the organizational level, no single work stands out like these two, but the works of Senge, Argyris&Schon and Nonaka&Takeuchi is recommended. What all these have in common is a focus on the practical “scene of action” where work is performed. This is also our focus, and the different modeling language constructs outlined in this post attempts to capture data about the “scene of action” in a way most fertile for future action and learning.

      So to answer your questions briefly:
      1. What are the components of Knowledge Architecture?

      This post descibes the basic building blocks in our language for active knowledge architectures, but users will have to extend these with their own concepts, closer to their scene of action.

      2. What is the difference between Knowledge Architecture and Knowledge Management?
      Knowledge management means a lot of different things to different people. Knowledge architecture is an IT-oriented approach to knowledge management that is based on social and organizational science.

      3. Is there any questionnaire on Knowledge Architecture?
      No, our work is primarily about designing practical IT tools, not empirical research.

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