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LikeInMind Conceptual Network

Page history last edited by Dmitry Sokolov 7 years, 3 months ago

 

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IVAN AIMS UCS LikeInMind PBWorks   Introduction to LikeInMind  

 

LikeInMind is a platform for arranging information for highest possible Findability, quickest possible access to the particular topics of interest at the particular moment of time. High rate of findability is provided by following measures:

  • AIMS: information is broken down with the fragments size adequate to future application and presented as topics. Each topic has a unique name
  • Topic Search is the way of finding a particular topic 
  • IVAN: topics are interlinked into the virtual associative network to reflects the individual associative networks existing in minds of LikeInMind users
  • Tag Clouds consist of topics with semantically close titles. Each of Sense Domains is formed from the "core" topics "surrounded" by the Tag Couds. This Many-to-One type of relationships ensure access to particular Sense Domains with no more than 2 clicks of mouse
  • Unified Conceptual Space: Tag Clouds and Virtual Associative Network are formed within a single and the only address space to provide a single point of access and to reduce uncertainty caused by the Fragmentation of Conceptual Space
  • Synchronisation of Virtual Associative Networks is a method and practice of sharing information between participants. High findability rates are ensured by "learning" process, and namely by crossmatching of associative networks of participants in such a way that information from all participants can be accessed directly by anyone from the group who is authorised
  • LikeInMind Taxonomy with the root here: Top. Taxonomy doesn't pretend to be "universal" and "common" as not possible according to Theory of Classification and Incompleteness Theorem. Synchronisation of Virtual Associative Networks and integration into IVAN is proposed instead.

 

https://www.facebook.com/notes/the-ecology-of-systems-thinking/introducing-your-projects/916154898463655

Connecting People by Connecting Their Knowledge

Problem to Solve:

Findability: ability to find information of particular interest when it is needed by authorised group of people (including mix of confidential and open information) for collective intelligence, P2P information sharing, self-organised projects development and management, and similar purposes

Significant reduction of time spent on finding known solutions, on search for experts in the particular topic, on solving problems and development of projects, as the result

Reframing of the content and opinions, distillation of conversations and discussions, and generation of new sense by the criteria of maximal efficiency, usability and applicability to the problem of current consideration.

Project (WorkingPrototype):
Number of topics (nodes of knowledgenetwork): >24,000

Finding capabilities:

The measured timespent on finding each particular item or topic is 5-10 seconds thatis close to the theoretical limit of 2-3 seconds. The time includeshuman-computer interaction. It is counted from the reception of arequest by a problem solver (or his own idea) and till the answer/solutionfound in the knowledge network, appeared on the screen.

Searchingcapabilities:

Topic search –PBWorks topic search

Semantic search –original Tag Cloud approach

Full text search –Google Custom search and/or PBWorks internal search

Browsingcapabilities:

Going up and downthe taxonomy levels

Going sidewisewithin the taxonomy levels

Followingindividual's (or “my own”) associative links between theitems/topics

Publishing capabilities:

The measuredpublishing rate for is 1-5 minutes per topic that will be improved atapplying AI algorithms. Artificial Intelligence will look forsemantic match with already published content, suggest ways ofweaving the topics into the knowledge network and taxonomy-likestructure. The later is currently done manually.
The mechanismof non-conflicting merging, or synchronisation of individualknowledge networks into a unified model with “different points ofview” is realised and tested.

List of experts in avery particular topic is found as quick as the topic.

To Be Done:

Kumu-likerepresentation of knowledge networks to make the work with it morevisual.

Marketing of theproject and finding investment opportunities

Computer added (AI)building of knowledge network and taxonomy

How Knows What:Development of the proposed mechanisms of knowledge clustering andauto-correlation of expertise of each participant to the particularfields of knowledge by his contribution to the content and structureof the knowledge network.

P2P client for evenfaster and fault-free access to the knowledge network replicated onyour personal computer and synchronised.

Example:

http://confocal-manawatu.pbworks.com/w/page/102462259/LikeInMindvs Kumu


 

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