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Visual Knowledge, Inc.

Visual Knowledge, Inc. Semantic Wikis Expedition #52 Conor Shankey CEO July 18, 2006. Large scale multi-agent software systems. Agents that are rapidly modeled and evolved by different groups of people. Visual Knowledge. What is our technology?. Visual Knowledge.

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Visual Knowledge, Inc.

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  1. Visual Knowledge, Inc. Semantic Wikis Expedition #52 Conor Shankey CEO July 18, 2006

  2. Large scale multi-agent software systems Agents that are rapidly modeled and evolved by different groups of people Visual Knowledge What is our technology?

  3. Visual Knowledge • R&D through real-world implementations

  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

  5. 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 962 000+ articles

  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

  7. Compared To… • Each person edits a copy of the document • A poor soul merges the results • Expensive file shares • E-mailing bulky documents • “Versions” of opaque documents everywhere • “Organizing” documents in hierarchal file system • Highly structured and closed database system version?

  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?

  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

  10. How do you do semantics? • Generalization • organizing concepts by kind • Aggregation • Aggregating complexes into simpler concepts • Common Properties • Relationships (connecting properties) • Attributes (flat properties) • Naming Conventions • Terms / Phrases • Language

  11. Taxonomies and Vocabularies • Close • One hierarchy of terms of concepts • Permit only one accepted notion of a term Brother?

  12. What else do semantics provide? • Contextual Meaning • Inferred Relationships • Causality • Granularity

  13. Semantics to the rescue • Phrases having different meanings in different contexts Water on mars? Food Space Exploration

  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

  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

  16. Social Network Person Details

  17. Semantic Search

  18. Ontology Management

  19. Custom View Editor

  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

  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

  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

  23. OWL Driven App Demo Semantic Wiki • If you would like to beta, please contact • cshankey@visualknowledge.com

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