Aggregating Operational Knowledge in Community Settings

Srinath SrinivasaOpen Systems Laboratory

IIIT BangaloreIndia

sri@iiitb.ac.inhttp://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!