1. Semantic Wikis Expedition #52 Conor Shankey CEO July 18, 2006
Visual Knowledge, Inc.
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2. Visual Knowledge What is our technology?
Large scale multi-agent software systems
Agents that are rapidly modeled and evolved by different groups of people
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3. Visual Knowledge
R&D through real-world implementations
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4. The original wiki idea
“A web site where anybody could create/edit a web page”
Structure
is not pre-determined
invented & evolved by community
neither top down or bottom up
Quick collaborative writing
Non-linear Hypertext
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5. Simplicity Additional Notions Very simple markup for authors
Any page can be immediately revised assuming you have the right privileges
All changes are audited and transparent to the community
“Concepts” in text can immediately become active resources (pages/links)
Simplicity
articles
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6. Benefits of the wiki idea
Distinct concepts or topics are built on the fly
Discourse forms around or in the context of a topic
Eliminates serialized document work flow
Team or community members can immediately see commentary in the context of a topic
Consensus
Agility
Cohesion
Speed
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7. version? Compared To… Each person edits a copy of the document
A poor soul merges the results
Expensive file shares
ing bulky documents
“Versions” of opaque documents everywhere
“Organizing” documents in hierarchal file system
Highly structured and closed database system
version?
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8. Greatest Strength and Weakness
Topics or concepts lack semantics
A WikiWord is just a WikiWord
A page with related formatted text and WikiWords
Authored, versioned content
Instance based security
Arbitrary structure
Quick and open architecture and adoption led to lack of standardization
Security?
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9. Semantic? (It’s what we do every second of the day.)
Convert data into something we can comprehend
By developing or applying concepts
Quickly relating them to instances in the world
Applying and revising our world models
Sharing our models with others
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10. How do you do semantics? Generalization Aggregation Common Properties
organizing concepts by kind
Aggregation
Aggregating complexes into simpler concepts
Common Properties
Relationships (connecting properties)
Attributes (flat properties)
Naming Conventions
Terms / Phrases
Language
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11. Taxonomies and Vocabularies
Close
One hierarchy of terms of concepts
Permit only one accepted notion of a term
Brother?
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12. What else do semantics provide?
Contextual Meaning
Inferred Relationships
Causality
Granularity
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13. Semantics to the rescue
Phrases having different meanings in different contexts
Water on mars?
Food
Space Exploration
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14. What can semantics bring to a wiki?
Different concepts have different properties
Spatial properties
Unique Relationships
Provide rich governance and security
Some information should be presented in views to different audiences (like your social security number)
For mission critical systems, staging is essential, we need separate, federated
Development servers
Data Staging servers
QA servers
Simulation servers
Production servers
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15. VK Semantic Wiki Wiki concepts are just elements of an ontology
Now wiki concepts have formal properties
Enables semantic searching
Security becomes deeply integrated into wiki
Customized Wiki Views
Driven by ontology
Driven by library of visual templates
Integrated change management
Federated semantic wikis
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16. Social Network Person Details
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17. Semantic Search
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18. Ontology Management
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19. Custom View Editor
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20. VK Semantic Wiki
Semantic Wikis are organized around Communities of Interest
Versions of ontologies of interest drive capabilities in Wiki
Protected worlds with controlled access to outside communities
All contacts, concepts, layouts presented in W3C standards
OWL
RDF (FOAF, Dublin Core)
HTML
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21. Use Case 1 Large public company with complex compliance concerns
Compliance documents are pasted into Semantic Wiki
Knowledge modelers develop/evolve underlying ontologies (semantic models) of organization, entitlement, responsibilities, etc..
Subject matter experts select text in wiki and convert it into semantics
Ontology drives structured compliance applications
Explicit Interpretation of Policy is mapped to applications
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22. Use Case 2 Spatial Ontology Working Group
Spatial information is in 80% of data
Diverse set of modelers from various government agencies, industry and academia
Very diverse multi-disciplinary coverage
Many different ontologies with overlap and different purposes
Need for organized discourse around events and around concepts
Need to integrate and harmonize models
Need for deeply integrate change management
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23. Demo Semantic Wiki OWL Driven App cshankey@visualknowledge.com
If you would like to beta, please contact
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