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KnowFlow in a visual knowledge management enviroment for collaborative groups. Collaborative SAAS, purely web-based multi-user application.

Although it is been created as a part of Neuroweb platform, it seems, that roots behind Douglas Engelbart conceptual framework and Neuroweb conceptual framework are the same. As one can see when reading D.Engelbart’s  group documents, cognitive science concepts were used in a context very similar to what is considered to be a conceptual framework of Culturalhistorical psychology approach, based on undestanding of concept developement and role of symbolic representations in development of psyche and culture. One can see citations of authors connected to cultural-historical psychology throughout the works of Engelbart’s group, i.e. of Alan’s Keys and other members.

KnowFlow system as it is and in scope of it’s developement is a system that can easily be understood via DKR conceptual approach. It does not mean that it was build on D.Engelbarts framework alone, but still all the basic concepts of DRK are there.

We build KnowFlow to be a living knowledge network, providing means to import, create, modify and view and export knowledge in a process of any complex product or project developement by a group of knowledge workers. That is basically a tool to boost group’s IQ, its collective exocortex.

KnowFlow can import different kinds of documents, including texts, maindmaps, presentations, media materials. We can do it via direct import, import from connected sources via public API, or via WebClipper that works as a Chrome Browser extention similar to one of Evernote, that can grab a page, fragment of text or a screenshot to save it as a knowledge element, rich format note. If the initial imported document is too large or consists of many subdocuments, special import algorythm can parce it up to a set of knowledge elements, saving each of them separately. Semantic engine is used to help user to parce the documents, finding the key statements in text, separating them and even instantly creating a concept-map our of the text.

User can work individually or with a group of other users on this knowledge material in different ways:

He can work with element as a text, using simple text editor, adding additional materials to a body of the knowledge element, making a group of elements form one element or combining several elements into a group that can be presented as a group or as a complex element at the same time. One can also use element (card) templates to make his work easier.

User can also work with his collection of knowledge as with a database, searching through elements, filtering them,  group or combine them in a “collection view”.

One can use domain system to tag or classify elements of knowledge he is working with. Domain is a set of classes that can also me named ontology. So, user can place elements in context of different ontologies.

User can work with elements or groups of elements (that can be seen as fractal elements) in a concept-map view. It means that one can connect elements with cinnection, set relations between them and work with them as one works with knowledge maps.

One card (knowledge element) can be used in different maps, still being the same element, changing in all the maps it is being used it. Or elements can be copied or modified, That way the system will know who and how used elements to build a new knowledge upon them. Version control is also there.

One can use his collection view not for his whole collection but for one knowledge space (map). This view disregards connections, still providing means to work with groups of cards and stacks or sets, similar to Kanban decs used in Trello (and yes we have an integration with trello. too)

Knowledge spaces (maps) can be connected to each other through “portals” – these are cards (elements) or groups of cards that are present in several maps (knowlege spaces) at the same time. This way knowledge workers can create a multi-space flow of knowledge between different maps, belonging to one or many groups, working with different aspects of knowledge and different stages of its life-cycle. A special instrument – “Сass” can be used to import knowledge elements or portals from other maps during group work.

Users can instantly view the map (knowledge space) in presentation view, when each element is one slide. navigating through complex knowledge structure, choosing different trajectories depending on what they want to focus on.

Additional collaboration tools include, but not limited to voting, commentaries end collective editing of knowledge in any view, as well as using shared knowledge space templates for different kinds of collaboration (brainstorms, SWOT analisys etc), collaborators can share elements of knowledge, collections, maps or it branches between individual users, groups or make them publicly available.  

All that work is augmented with voice recognition that lets users speak their knowledge in – and that is particularly useful in capruring debates or meetings. All functions of the KnowFlow system are augmented with machine learning semantic recommendation engine. That recommends fragments of knowledge already in the system, recommends templates and connections in knowledge map mode. We also have a Virtual Relity interface that can give one an opportunity to view multiple knowledge spaces at the same time, walk between knowledge networks and see them being created in real time.

All the results can be exported in any moment to a number of formats (including PDF, Mindmanager, Table, Text, etc)  or streamed through API to other elements for additional ways of use. One of uses of KnowFlow is creation a templates or publically available knowledge spaces, knowledge elements, maps or chains that contain best practices and knowledge that can be used in other contexts, when needed.

Next version will also include timeline views usefull for sorting all the knowledge elements on a timeline. filtered by meta-data, VR interface will get edit capabilities and voice commands will be available for a group work in shared spaces. Additional libraries of semanic analisis will be added, text edit mode will be upgraged, and number of other software integrations will provide easy use of KnowFlow as a collaborative knowledge processing tool in any workflows.

That is, basically, DKR, as it seems to me.

“A dynamic knowledge repository is a living, breathing, rapidly evolving repository of all the stuff accumulating moment to moment throughout the life of a project or pursuit. This would include successive drafts and commentary leading up to more polished versions of a given document, brainstorming and conceptual design notes, design rationale, work lists, contact info, all the email and meeting notes, research intelligence collected and commented on, emerging issues, timelines, etc.”
That are the forms of data that can be inmorted, used, transformed and contextually accessed, History tool provides ability to move back in time to a point in knowledge space developement, to access relevant data and fork new line of work from there, still being able to use all the knowledge being created later or in lateral lines of work, even by other groups, proven that access mode for them makes it available.

One can say that knowflow is created as a version of CoDIAK process, as it supports and speeds up ways to develop, integrate, and apply all this iterating knowledge from the swirl of disparate concurrent contributions. So, if KnowFlow is an “emerging collective record of all this activity captured on the fly — the emerging collective vision, know-how, the group brain, memory, where the dots are connected and the right hand knows what the left hand is doing” it means that KnowFlow is really a version of DKR .

KnowFlow emerged as a result of best practices of work with project groups in business schools, methods and approaches to collective work (i.e. Rapid Foresight methodology, co-developed by KnowFlow team members, etc) and is already used to facilitiate the collective work “in a way that optimizes the capture, organization, and enhanced utility of the DKR”. Best practices would include special roles such as collaboration facilitators, as well as high performance knowledge specialists, who are continuously moderating the connectivity, capture, tagging, tracking, and portals into the emerging repository — tools, practices, and people connecting in a way that renders the DKR optimally navigable, searchable, and useful.

Relevance to Doug Engelbart ‘s work

  • DKR

Presented By

Timour Shoukine and Irina Antonova


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