Aggregating Operational Knowledge in Community Settings
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Aggregating Operational Knowledge in Community Settings
Srinath SrinivasaOpen Systems Laboratory
IIIT BangaloreIndia
[email protected]://osl.iiitb.ac.in/
Problem Setting..
Image Source: Wikipedia
Problem Setting..
Image Source: Wikipedia
Problem Setting..
Consolidating pertinent knowledge in loosely structured environments..
Image Source: Wikipedia
Commercial Clusters
● No organizational structure● Individual shop owners join cluster autonomously● No overarching reporting structure● Collective action taken by consensus
● More organized than a crowd● All shop owners have something in common● Shared interests to collaborate and compete
● Communities: Generalization of Commercial Clusters
Communities, Organizations and Crowd
Organization
Structure:
Highly structured
Motivation:
Occupation, shared vision
Affiliation:
Formal
Knowledge dynamics:
Top-down
Communities, Organizations and Crowd
Organization
Structure:
Highly structured
Motivation:
Occupation, shared vision
Affiliation:
Formal
Knowledge dynamics:
Top-down
Crowd
Structure:
Unstructured
Motivation:
Herd instinct
Affiliation:
Informal and/or transient
Knowledge dynamics:
Diffusion models
Communities, Organizations and Crowd
Organization
Structure:
Highly structured
Motivation:
Occupation, shared vision
Affiliation:
Formal
Knowledge dynamics:
Top-down
Community
Structure:
Loosely structured
Motivation:
Shared human condition
Affiliation:
Semi-formal
Knowledge dynamics:
Bottom-up
Crowd
Structure:
Unstructured
Motivation:
Herd instinct
Affiliation:
Informal and/or transient
Knowledge dynamics:
Diffusion models
Operational Knowledge
● Actionable knowledge elements● “knowledge that works”● Contrasted with encyclopedic knowledge or
“knowledge that tells”
Encyclopedic Knowledge
Local perspectives
Encyclopedic Knowledge
Encyclopedic knowledge
Local perspectives
Encyclopedic Knowledge
Aggregates several local perspectives to a global whole
A convergent process of aggregation
No subjective versions
Quality based on balancing POVs
Encyclopedic knowledge
Local perspectives
Operational Knowledge
Well knownCommon
Knowledge
Operational Knowledge
Well knownCommon
Knowledge
Utility 2Utility 1
Utility 3
Operational Knowledge
Aggregates a set of common knowledge into different local utilitarian “worlds”
Subjective by definition. User is a part of the encoded knowledge rather than an outside observer
A divergent process of “aggregation”
Well knownCommon
Knowledge
Utility 2Utility 1
Utility 3
Operational Knowledge
● Most common to dynamics of communities
● Concerned with putting a set of common knowledge to different uses
● Subjective by definition: what is utilitarian to one need not be utilitarian to another
● User (consumer of knowledge) part of the encoded knowledge base rather than an outside observer
● A divergent process: communities necessarily dilute their common condition by utilizing it in different (interrelated) ways
Aggregating Operational Knowledge
Essential requirements of operational knowledge app:
Support a divergent phenomena with minimal redundancies
Support mechanisms to fill cognitive “holes” in a divergent process
Many Worlds on a Frame (MWF)
● Proposed data model for capturing a divergent knowledge aggregation phenomena
● Partially implemented in an application called RootSet (http://rootset.iiitb.ac.in/)
● Expressible as a superposition of two modal Frames in Kripke semantics (a posteriori analysis)
MWF: Frame
Only global data structure
where
MWF: Frame
Concept hierarchy
Inherits properties, associations and world structure
Rooted in a concept called Concept
Containment hierarchy
Inherits privileges and visibility
Rooted in a concept called UoD
MWF: WorldA world is a concept that can host relationships between concepts and host “Resources” (Files, Media, Web links, RSS feeds, etc.)
Concepts participating in a world are “imported” from the Frame and play a “Role” in the World
Roles are connected with one another with “Associations”
University
Org Unit
Department
Activity
PersonPerson
Faculty
Course
Student
is-in is-a
MWF: Instances
● Any concept that cannot be subclassed is called an Instance
● In any instance of a world, a relationship instance can be added between two concept instances, iff a relationship type exists between the respective concepts in the world type ancestry
MWF: Privileges
● Users and privileges an integral part of operational knowledge
● MWF privileges broadly ordered into following levels:● Frame-level privileges● Structure-level privileges● Data-level privileges● Visibility privileges
● Privileges are inherited through the is-in hierarchy
● A user having privilege p in concept C will have a privilege at least p in all concepts contained in C
MWF: World Creation
New worlds can be created in the following ways:● Simpliciter
Create and manually specify lineage (is-a, is-in ancestry)● Clone
Create new world with same structure and is-a ancestry, specify is-in ancestry manually
● InduceCreate new world within an existing world by inducing a new world around a part of the structure. Specify is-a ancestry manually
Cognitive Gaps
● Divergent phenomena entails knowledge base forking off in different directions● Diversified attention● Reason for communities to be less efficient than
organizations● Possibility of emergence of “Cognitive gaps” --
elements of knowledge that get left out because attention is diversified
● Need for Cognitive “gap fillers” -- semantic recommendations by the knowledge base
Cognitive Gap Fillers
Heuristics to suggest knowledge elements to fill cognitive gaps:
Data level heuristics● Principle of locality of relevance
– Instances that play a role in a world are typically found in the vicinity of the world itself
● Birds of a Feather principle– Similar instances play similar roles in similar worlds
Cognitive Gap Fillers
● Data level heuristics● Resource diffusion principles
– Resources in a world are typically relevant to concepts that play a role in the world
– Resources held by a concept playing a role in a world are typically relevant to other concepts playing similar roles
● Structure level heuristics● Triadic closure
– If concept A is related to concepts B and C in a world, the greater the strength of the association by virtue of number of instances, the greater the possibility that B and C are semantically related
Cognitive Gap Fillers
● Structure level heuristics● Clustering principle
– Concepts tend to form semantic clusters where association among elements of a cluster are tighter than associations across clusters
Thank You!