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Semantic Wikis
     Social Semantic Web In Action


                    2011-03-25
Specially Prepared for Tsinghua University Alumni
 in greater Seattle area for centennial celebration
About Me: Jesse Wang 王(嘉)欣




2
Who is Vulcan




3
What does Vulcan do




4
It all began with a vision…




5
Now the Vision Continues as Project Halo




6
Project Halo’s Focus Areas


                                 • Automated User-Centered
              AURA                 Reasoning and Acquisition System
                                 • Text book you can talk to


                                 • Semantic Inference with Large
              SILK                 Knowledge-base
                                 • Non-monotonic rule system / RIF


                                 • Semantic MediaWiki +
              SMW+               • Knowledge authoring with SMEs



          Plus other related semantic technologies and commercial efforts
7
Project Halo’s Goals

       Address the core problems
        in Knowledge Bases
                       – scale
                       – brittleness
       Have high impact
        KB Effort (cost, people,…)




                                                         Now



                                                               Vulcan


                                                                    Future

                                     KB size (number of assertions, complexity…)




8
Crowdsourcing for Better Knowledge Acquisition




9
Wiki as a Crowdsourcing Tool




               This distinguishes wikis from other publication tools


11
Consensus in Wikis Comes from

        Collaboration
         – ~17 edits/page on average in
           Wikipedia (with high variance)
         – Wikipedia‟s Neutral Point of View




        Convention
         – Users follow customs and
           conventions to engage with
           articles effectively


12
Software Support Makes Wikis Successful

        Trivial to edit by anyone
        Tracking of all changes, one-
         step rollback
        Every article has a “Talk” page
         for discussion
        Notification facility allows
         anyone to “watch” an article
        Sufficient security on pages,
         logins can be required
        A hierarchy of administrators,
         gardeners, and editors
        Software Bots recognize certain
         kinds of vandalism and auto-
         revert, or recognize articles that
         need work, and flag them for
         editors

13
Success of Wikis




14
Wikis are Great, But…

                             Wiki Clock?




15
How About Hidden Goodies in the Wiki?


Wikipedia has articles
about…
      •… all cities
      •… their populations
      •… their mayors
      •… the skyscrapers
So can I ask for a list of
the world‟s 5 largest
cities with a female
mayor?
Or Skyscrapers in
Shanghai with 50+ floors
and built after 2000?


                                        16
Enters Semantics…
     To answer questions like:
     •   The female majors of top 10 cities,
         sorted by population, starting year,
         age…
     •   All skyscrapers in China (Japan,
         Thailand,…) of 50 (40/60/70) floors or
         more, and built in year 2000
         (2001/2002) and after, sorted by built
         year, floors…, grouped by cities,
         regions…
     •   Median (average) base annual salary
         of CEOs of Fortune 100 companies in
         America (Europe, Asian,…)
     •   All Porsche Vehicles Made in Germany
         that accelerate from 1-100 km/h less
         than 4 seconds
     •   Sci-Fi movies made after year 2000
         that cost less than $10M and gross
         more than $30M
     •   A map showing where all Mercedes-
         Benz vehicles are manufactured
     •   And many more



17
What is a Semantic Wiki

      A wiki that has an underlying model of the
       knowledge described in its pages.
      To allow users to make their knowledge explicit and formal
      Semantic Web Compatible




                                                   Semantic Wiki




18
Two Perspectives


         Wikis for
         Metadata

         Metadata for
         Wikis
19
Characteristics of Semantic Wikis



                                    Semantic Wikis




                                                     20
List of Semantic Wikis

AceWiki                       Semantic MediaWiki - an
ArtificialMemory              extension to MediaWiki that
Wagn - Ruby on Rails-based    turns it into a semantic wiki
KiWi – Knowledge in a Wiki    Swirrl - a spreadsheet-based
                              semantic wiki application
Knoodl – Semantic
Collaboration tool and        TaOPis - has a semantic wiki
application platform          subsystem based on Frame
                              logic
Metaweb - the software that
powers Freebase               TikiWiki CMS/Groupware
                              integrates Semantic links as a
OntoWiki                      core feature
OpenRecord                    zAgile Wikidsmart - semantically
PhpWiki                       enables Confluence
                                                           21
Basics of Semantic Wikis

        Still a wiki, with regular wiki features
         – Category/Tags, Namespaces, Title, Versioning, ...
        Typed Content (built-ins + user created, e.g. categories)
         – Page/Card, Date, Number, URL/Email, String, …
        Typed Links (e.g. properties)
         – “capital_of”, “contains”, “born_in”…
        Querying Interface Support
         – E.g. “[[Category:Member]] [[Age::<30]]” (in SMW)




22
SMW Markup Syntax




                 Tsinghua is a university located in
                   [[Has location::Beijing]], with
                [[Has population::27,000]] students.


       In page "Property:Has location":   In page "Property:Has population":

         [[Has type::Page]]                 [[Has type::number]]




24
Define Classes

      On Page Beijing a city in [[Has
             Beijing is
             country::China]], with population
      One possible solution:
             [[Has population::2,200,000]].
          – Beijing is a [[Is a::city]]
               [[Category::Cities]]

      Categories are used to define classes because they are better for class inheritance.


     The Jin Mao Tower (金茂大厦) is an 88-story landmark supertall
     skyscraper in …

     [[Categories: 1998 architecture | Skyscrapers in
     Shanghai | Hotels in Shanghai | Skyscrapers over 350
     meters | Visitor attractions in Shanghai | Landmarks in
     Shanghai | Skidmore, Owings and Merrill buildings]]


     Category:Skyscrapers in China     Category: Skyscrapers by country

26
Database-style Query over Wiki Data

        Example: Skyscrapers in China
       higher than 50 stories, built before
                      2000

              ASK/SPARQL query target
      {{#ask:
         [[Category:Skyscrapers]]
         [[Located in::China]]
         [[Floor count::>50]]
         [[Year built::<2000]]
          …
      }}



27
What is the Promise of Semantic Wikis?

 Semantic Wikis promise
  Consensus over Data
 Combine low-expressivity
  data authorship with the
  best features of traditional
  wikis
 User-governed, user-
  maintained, user-defined
 Easy to use as an
  extension of text authoring


                                         29
One Key Helpful Feature of Semantic Wikis




                 Semantic Wikis are “Schema-Last”
                    Databases require DBAs and schema design;
              Semantic Wikis develop and maintain the schema in the wiki


31
Semantic MediaWiki in 2010

        Open source (GPL)
        Well documented
        Active mailing list
        Commercial support available
        World-wide community
        Regular Conferences
         – Next SMWCon 4/28-30, 2011 Arlington, VA




                             Very stable SMW core
                      Mature while still growing, slowly but steadily


32
SMW Extensions

         Data I/O

        • Halo Extensions, Semantic Forms, Semantic Notification, …

         Query and Browsing

        • Semantic Toolbar, Semantic Drilldown, Enhanced Retrieval, Search…

         Visualization

        • Semantic Result Printers, Tree View, Exhibit, Flash charts…

         Other useful extensions

        • HaloACL, Deployment, Triplestore Connector, Simple Rules…
        • Semantic WikiTags and Subversion Integration extensions
        • Upcoming Linked Data Extension, with R2R and SILK from F.U.Berlin

33
Wikis Can Help Information Management

         Research = Locate and Find Data ?

 Business Intelligence
 Finding Expertise
 Internal Encyclopedia
 Documentation
 Enterprise Search


        Crowd Sourcing is a Great Solution!
                                              37
Example I: KnowIT in Johnson & Johnson

   Most Frequently Asked Questions: (J&J example)
        –    What are the directions between two J&J sites?
        –    What is the meaning of KOL ? HLM ? DRU ?
        –    What data sources can we use to compare biological pathways?
        –    Can you give us a list of R&D applications, related servers and
             stakeholders and send us an update every six months?
   Capture Facts About Things
    –       Definitions, concepts, questions
    –       Locations
    –       Data sources
    –       Organizations and people
    –       Technologies and systems
                                                                               38
System Architecture




                      39
Example II: Knowledge Encapsulation Framework

 Allow modelers to exploit the „information resources‟ they
  have and discover new, potentially relevant material across
  new media types
 KEF aims to provide:
    – an effective method for storing, retrieving, reviewing and
      annotating your documents
    – an environment where you can share these materials with team
      members and discuss
    – a mechanism to discover new, related information for social and
      traditional media
    – a means to link this material to model representations to aid
      analysis and game-play
   Achieved by a semantic wiki enabled with an NLP pipeline
                                                                        41
42
43
Example 3: Ultrapedia – An Analytical Semantic Wikipedia

        Ultrapedia: An SMW demo built to explore general knowledge
         acquisition in a wiki
        Wikipedia merged with the power of a database
         – Data extracted from Wikipedia Infobox and Table data; stored in RDF
         – For Authors: tools to create more compelling articles
              •   Great visualizations: charts, tables, timelines, photos, analytics
              •   Always up-to-date across the Encyclopedia
              •   Encourage data consistency and find data errors
              •   Link in other web data sources
         – For Readers:
              • Enhanced articles and data interaction
              • Faceted navigation
              • Sophisticated queries (both standing and ad-hoc)

        Maintenance via the Wikipedia update process
         – Data is from the article text, with simple ways for article authors to maintain and
           extend it.
         – Authors and readers always in the loop for merging, updating, validating,
           mapping
45
Graph Views of the Acceleration Data
Dynamic Mapping and Charting
Information Discovery via Visualization




52
Video: Semantic Wikis for A New Problem

                               Increasing technical complexity →
                                 ← Increasing User Participation




        Social tag-based                                             Algorithm-based
                                     Semantic
         characterization                                              object
                                   Entertainment
        Keyword search over            Wiki                           characterization
         tag data                                                     Database-style
        Inconsistent             Social database-style               search
         semantics                 characterization                   Consistent semantics
        Easy to engineer         Database search +                  Extremely difficult to
                                   wiki text search                    engineer
                                  Semantic consistency
                                   via wiki mechanisms
                                  Easy to engineer

55
Semantic Seahawks Football Wiki




56
Based on Simple Templates and Forms




57
Semantic Entertainment: Query Result  Highlight Reel

                                              Commercial
                                               Look/Feel
                                              Play-by-play
                                               video search
                                              Highlight reel
                                               generation
                                              Search on
                                               crowd-defined
                                               patterns
                                               (“touchdowns
                                               with big hits”)
                                              Tree-based
                                               navigation
                                               widget
                                              Very favorable
                                               economics
The Inspiration

        We started with a



        We built a




        We now have an



60
We CAN Build Applications (Fairly) Easily

        With all the extensions of Semantic MediaWiki.
           Data I/O

           • Halo Extensions, Semantic Forms, Semantic Notification, …

           Query and Browsing

           • Semantic Toolbar, Semantic Drilldown, Enhanced Retrieval, Search…

           Visualization

           • Semantic Result Printers, Tree View, Exhibit, Flash charts…

           Other useful extensions

           • HaloACL, Deployment, Triplestore Connector, Simple Rules…
           • Semantic WikiTags and SVN Integration extensions
           • Upcoming Linked Data Extension, with R2R and SILK from FUB



61
Collaborative Proposal Management at BT with SMW+




                                       Active Bid Viewer
                                      Service Desk Selector




65
Social Semantic Web Applications




     Omitting x examples, y pictures and z lines of text…




67
Case Study 2 and Demo: Project Management with SMW+


                                            Automatically
                                             populate tables
                                            Just the data you
                                             want,
                                            At the level you want
                                            Calendars and
                                             timelines
                                            Workflows
                                            Personal menus
                                            Form-oriented inputs
                                            Notifications via
                                             email/RSS
                                            MS Office integration
                                            SVN integration


68
Vulcan Project Management Wiki (Story)
Vulcan Project Management Wiki (Task)




70
Vulcan Project Management Wiki (Visualizations)




71
Screenshot of a Sprint page




             Data automatically generated via template queries on page

               http://wiking.vulcan.com/dev/index.php/Sprint_101020

72
Requirements for Wiki “Developers”

        One need not
         – Write code like a hardcore programmer
         – Design, setup RDBMS or make frequent schema changes
         – Possess knowledge of a senior system admin
        Instead one need
         – Configure the wiki with desired extensions
         – Design and evolve the data model (schema)
         – Design Content
             • Customize templates, forms, styles, skin, etc.
        The bar is dramatically lowered to build applications
         – “Source code” is part of the open content of wiki too!

73
Effectiveness of SMW as a Platform Choice


      Packaged Software      SMW + Extensions          Custom Development
     ☺Very quick to          ☺ Still quick to          N Slow to develop
     obtain                  program                   ☺Extremely flexible
     N Hard to customize     ☺ Easy to customize       N High cost to develop
     N Expensive             ☺ Low-moderate cost       and maintain

        Microsoft Project      Vulcan Project Wiki      .NET Framework
        Version One            B.L.S.                   J2EE, …
        Microsoft              RPI map                  Ruby on rails
         SharePoint



74
Conclusions

        Semantic MediaWiki+ (http://smwforum.ontoprise.com)
         –   Open-source, growing semantic wiki software system
         –   Wiki-style text + semantic markups
         –   Collaborative, user-governed subject models and data curation
         –   Simple and extensible data models with easy import/export
        SMW+ has many government and industry users
         – People built applications with it
         Knowledge Management via



                                                   KB Effort (cost, people,…)
                                                                                                   Now
         crowds can work
         – A way to leverage and exploit
           web-collected data                                                                             Vulcan
         – A lightweight collaborative
           knowledge management tool                                                                            Future

        A new platform for lightweight                                         KB size (number of assertions, complexity…)


         web application development
79
Acknowledgement




                  80
(End of Slides)




     Backups starts here


81
Case Study: Battle-space Luminary System

        Discover when New Information represents a change in understanding of entities
          – Discovery of explicit entity links, implicit relationships
        Large Volumes of Data in various formats
          – Unstructured news articles
          – Tactical Reports, Field Intelligence
          – Structured Database Information
        Use Wiki Pages to represent current knowledge about an entity – “what we know”
        Domain Ontology to represent domain of information – “what we want to know”
        Issue Alerts when Significant Events occur
          – New information according to category
          – Changing information on topics of interest
          – Need to send information to various devices – cell phones, email, etc.




82
System Design

        Wiki Configuration
          – Semantic MediaWiki: Large developer community, active development, open
            source. Wikipedia uses MediaWiki, so scalability and performance are
            important.
          – Semantic Results Format: Provides various rich media displays of semantic
            information, including graphs, timelines, maps
          – Semantic Forms: Provides convenient user interface for entering semantic
            data into wiki, avoiding cumbersome wikitext
          – Semantic Notifications: Enables sending of notifications when results of
            semantic query change.
        Domain Ontology
          – Created OWL Ontology for Terrorism
        Semantic Parsing, Extraction, Reasoning
          – Java Process using various Open-Source Toolkits
          – Rapid plugin of new technologies
83        – Multiple Data Sources supported
Sample Content Page




84
Wiki Content Design

        Use Templates to Ensure Consistent Look-and-Feel
          – Templates Correspond to Ontology Classes
          – Fields within Templates correspond to Properties within Ontology
          – Rich Content Visualizations derived in consistent way
        Hierarchical Categories match Class Hierarchy within Ontology
          – Ensures Validity for Properties
          – Category included on each Template page to ensure consistency
        Forms Provide ability for users to enter data directly into wiki without
         knowing Wiki Text
          –   Each form corresponds to a Template
          –   Fields within forms correspond to the fields/properties within the Template
          –   GUI can include auto-completion
          –   Created Page immediately linked semantically to rest of Wiki


85
Sample Visualizations




86
Wikipedia for Porsches (Acceleration Data Example)




     Information Need: All Porsche models that accelerate 0-
      100kph in under 5, 6, and 7 seconds
More Porsche Acceleration Data in Wikipedia
Ultrapedia Main Page
  Main Page
Semantics for Improved Wiki Navigation



Tree View Control       Abstract/Summary quick preview
The Porsche 996 Acceleration Table In Ultrapedia
Same Table as a Query
Dynamically-Generated Tables forfast?
              Which Porsches accelerate
                                        Queries




      Information Need: All Porsche models that accelerate 0-
       100kph in under 5, 6, and 7 seconds
Graph Views of the Acceleration Data
External Data via a Live Ebay Query
Linking to External Ebay Data
Photos in Mercedes-Benz E-class W212 Gallery Section
          Wiki Articles as Data
Timelines from Data Production Timeline View
               Volkswagen
Dynamic Mapping and Charting
Editing Wiki Data In Place




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Semantic Wiki: Social Semantic Web In Action:

  • 1. Semantic Wikis Social Semantic Web In Action 2011-03-25 Specially Prepared for Tsinghua University Alumni in greater Seattle area for centennial celebration
  • 2. About Me: Jesse Wang 王(嘉)欣 2
  • 5. It all began with a vision… 5
  • 6. Now the Vision Continues as Project Halo 6
  • 7. Project Halo’s Focus Areas • Automated User-Centered AURA Reasoning and Acquisition System • Text book you can talk to • Semantic Inference with Large SILK Knowledge-base • Non-monotonic rule system / RIF • Semantic MediaWiki + SMW+ • Knowledge authoring with SMEs Plus other related semantic technologies and commercial efforts 7
  • 8. Project Halo’s Goals  Address the core problems in Knowledge Bases – scale – brittleness  Have high impact KB Effort (cost, people,…) Now Vulcan Future KB size (number of assertions, complexity…) 8
  • 9. Crowdsourcing for Better Knowledge Acquisition 9
  • 10. Wiki as a Crowdsourcing Tool This distinguishes wikis from other publication tools 11
  • 11. Consensus in Wikis Comes from  Collaboration – ~17 edits/page on average in Wikipedia (with high variance) – Wikipedia‟s Neutral Point of View  Convention – Users follow customs and conventions to engage with articles effectively 12
  • 12. Software Support Makes Wikis Successful  Trivial to edit by anyone  Tracking of all changes, one- step rollback  Every article has a “Talk” page for discussion  Notification facility allows anyone to “watch” an article  Sufficient security on pages, logins can be required  A hierarchy of administrators, gardeners, and editors  Software Bots recognize certain kinds of vandalism and auto- revert, or recognize articles that need work, and flag them for editors 13
  • 14. Wikis are Great, But… Wiki Clock? 15
  • 15. How About Hidden Goodies in the Wiki? Wikipedia has articles about… •… all cities •… their populations •… their mayors •… the skyscrapers So can I ask for a list of the world‟s 5 largest cities with a female mayor? Or Skyscrapers in Shanghai with 50+ floors and built after 2000? 16
  • 16. Enters Semantics… To answer questions like: • The female majors of top 10 cities, sorted by population, starting year, age… • All skyscrapers in China (Japan, Thailand,…) of 50 (40/60/70) floors or more, and built in year 2000 (2001/2002) and after, sorted by built year, floors…, grouped by cities, regions… • Median (average) base annual salary of CEOs of Fortune 100 companies in America (Europe, Asian,…) • All Porsche Vehicles Made in Germany that accelerate from 1-100 km/h less than 4 seconds • Sci-Fi movies made after year 2000 that cost less than $10M and gross more than $30M • A map showing where all Mercedes- Benz vehicles are manufactured • And many more 17
  • 17. What is a Semantic Wiki  A wiki that has an underlying model of the knowledge described in its pages.  To allow users to make their knowledge explicit and formal  Semantic Web Compatible Semantic Wiki 18
  • 18. Two Perspectives Wikis for Metadata Metadata for Wikis 19
  • 19. Characteristics of Semantic Wikis Semantic Wikis 20
  • 20. List of Semantic Wikis AceWiki Semantic MediaWiki - an ArtificialMemory extension to MediaWiki that Wagn - Ruby on Rails-based turns it into a semantic wiki KiWi – Knowledge in a Wiki Swirrl - a spreadsheet-based semantic wiki application Knoodl – Semantic Collaboration tool and TaOPis - has a semantic wiki application platform subsystem based on Frame logic Metaweb - the software that powers Freebase TikiWiki CMS/Groupware integrates Semantic links as a OntoWiki core feature OpenRecord zAgile Wikidsmart - semantically PhpWiki enables Confluence 21
  • 21. Basics of Semantic Wikis  Still a wiki, with regular wiki features – Category/Tags, Namespaces, Title, Versioning, ...  Typed Content (built-ins + user created, e.g. categories) – Page/Card, Date, Number, URL/Email, String, …  Typed Links (e.g. properties) – “capital_of”, “contains”, “born_in”…  Querying Interface Support – E.g. “[[Category:Member]] [[Age::<30]]” (in SMW) 22
  • 22. SMW Markup Syntax Tsinghua is a university located in [[Has location::Beijing]], with [[Has population::27,000]] students. In page "Property:Has location": In page "Property:Has population": [[Has type::Page]] [[Has type::number]] 24
  • 23. Define Classes  On Page Beijing a city in [[Has Beijing is country::China]], with population  One possible solution: [[Has population::2,200,000]]. – Beijing is a [[Is a::city]] [[Category::Cities]] Categories are used to define classes because they are better for class inheritance. The Jin Mao Tower (金茂大厦) is an 88-story landmark supertall skyscraper in … [[Categories: 1998 architecture | Skyscrapers in Shanghai | Hotels in Shanghai | Skyscrapers over 350 meters | Visitor attractions in Shanghai | Landmarks in Shanghai | Skidmore, Owings and Merrill buildings]] Category:Skyscrapers in China Category: Skyscrapers by country 26
  • 24. Database-style Query over Wiki Data Example: Skyscrapers in China higher than 50 stories, built before 2000 ASK/SPARQL query target {{#ask: [[Category:Skyscrapers]] [[Located in::China]] [[Floor count::>50]] [[Year built::<2000]] … }} 27
  • 25. What is the Promise of Semantic Wikis?  Semantic Wikis promise Consensus over Data  Combine low-expressivity data authorship with the best features of traditional wikis  User-governed, user- maintained, user-defined  Easy to use as an extension of text authoring 29
  • 26. One Key Helpful Feature of Semantic Wikis Semantic Wikis are “Schema-Last” Databases require DBAs and schema design; Semantic Wikis develop and maintain the schema in the wiki 31
  • 27. Semantic MediaWiki in 2010  Open source (GPL)  Well documented  Active mailing list  Commercial support available  World-wide community  Regular Conferences – Next SMWCon 4/28-30, 2011 Arlington, VA Very stable SMW core Mature while still growing, slowly but steadily 32
  • 28. SMW Extensions Data I/O • Halo Extensions, Semantic Forms, Semantic Notification, … Query and Browsing • Semantic Toolbar, Semantic Drilldown, Enhanced Retrieval, Search… Visualization • Semantic Result Printers, Tree View, Exhibit, Flash charts… Other useful extensions • HaloACL, Deployment, Triplestore Connector, Simple Rules… • Semantic WikiTags and Subversion Integration extensions • Upcoming Linked Data Extension, with R2R and SILK from F.U.Berlin 33
  • 29. Wikis Can Help Information Management Research = Locate and Find Data ?  Business Intelligence  Finding Expertise  Internal Encyclopedia  Documentation  Enterprise Search Crowd Sourcing is a Great Solution! 37
  • 30. Example I: KnowIT in Johnson & Johnson  Most Frequently Asked Questions: (J&J example) – What are the directions between two J&J sites? – What is the meaning of KOL ? HLM ? DRU ? – What data sources can we use to compare biological pathways? – Can you give us a list of R&D applications, related servers and stakeholders and send us an update every six months?  Capture Facts About Things – Definitions, concepts, questions – Locations – Data sources – Organizations and people – Technologies and systems 38
  • 32. Example II: Knowledge Encapsulation Framework  Allow modelers to exploit the „information resources‟ they have and discover new, potentially relevant material across new media types  KEF aims to provide: – an effective method for storing, retrieving, reviewing and annotating your documents – an environment where you can share these materials with team members and discuss – a mechanism to discover new, related information for social and traditional media – a means to link this material to model representations to aid analysis and game-play  Achieved by a semantic wiki enabled with an NLP pipeline 41
  • 33. 42
  • 34. 43
  • 35. Example 3: Ultrapedia – An Analytical Semantic Wikipedia  Ultrapedia: An SMW demo built to explore general knowledge acquisition in a wiki  Wikipedia merged with the power of a database – Data extracted from Wikipedia Infobox and Table data; stored in RDF – For Authors: tools to create more compelling articles • Great visualizations: charts, tables, timelines, photos, analytics • Always up-to-date across the Encyclopedia • Encourage data consistency and find data errors • Link in other web data sources – For Readers: • Enhanced articles and data interaction • Faceted navigation • Sophisticated queries (both standing and ad-hoc)  Maintenance via the Wikipedia update process – Data is from the article text, with simple ways for article authors to maintain and extend it. – Authors and readers always in the loop for merging, updating, validating, mapping 45
  • 36. Graph Views of the Acceleration Data
  • 38. Information Discovery via Visualization 52
  • 39. Video: Semantic Wikis for A New Problem Increasing technical complexity → ← Increasing User Participation  Social tag-based  Algorithm-based Semantic characterization object Entertainment  Keyword search over Wiki characterization tag data  Database-style  Inconsistent  Social database-style search semantics characterization  Consistent semantics  Easy to engineer  Database search +  Extremely difficult to wiki text search engineer  Semantic consistency via wiki mechanisms  Easy to engineer 55
  • 41. Based on Simple Templates and Forms 57
  • 42. Semantic Entertainment: Query Result  Highlight Reel  Commercial Look/Feel  Play-by-play video search  Highlight reel generation  Search on crowd-defined patterns (“touchdowns with big hits”)  Tree-based navigation widget  Very favorable economics
  • 43. The Inspiration  We started with a  We built a  We now have an 60
  • 44. We CAN Build Applications (Fairly) Easily  With all the extensions of Semantic MediaWiki. Data I/O • Halo Extensions, Semantic Forms, Semantic Notification, … Query and Browsing • Semantic Toolbar, Semantic Drilldown, Enhanced Retrieval, Search… Visualization • Semantic Result Printers, Tree View, Exhibit, Flash charts… Other useful extensions • HaloACL, Deployment, Triplestore Connector, Simple Rules… • Semantic WikiTags and SVN Integration extensions • Upcoming Linked Data Extension, with R2R and SILK from FUB 61
  • 45. Collaborative Proposal Management at BT with SMW+ Active Bid Viewer Service Desk Selector 65
  • 46. Social Semantic Web Applications Omitting x examples, y pictures and z lines of text… 67
  • 47. Case Study 2 and Demo: Project Management with SMW+  Automatically populate tables  Just the data you want,  At the level you want  Calendars and timelines  Workflows  Personal menus  Form-oriented inputs  Notifications via email/RSS  MS Office integration  SVN integration 68
  • 49. Vulcan Project Management Wiki (Task) 70
  • 50. Vulcan Project Management Wiki (Visualizations) 71
  • 51. Screenshot of a Sprint page Data automatically generated via template queries on page http://wiking.vulcan.com/dev/index.php/Sprint_101020 72
  • 52. Requirements for Wiki “Developers”  One need not – Write code like a hardcore programmer – Design, setup RDBMS or make frequent schema changes – Possess knowledge of a senior system admin  Instead one need – Configure the wiki with desired extensions – Design and evolve the data model (schema) – Design Content • Customize templates, forms, styles, skin, etc.  The bar is dramatically lowered to build applications – “Source code” is part of the open content of wiki too! 73
  • 53. Effectiveness of SMW as a Platform Choice Packaged Software SMW + Extensions Custom Development ☺Very quick to ☺ Still quick to N Slow to develop obtain program ☺Extremely flexible N Hard to customize ☺ Easy to customize N High cost to develop N Expensive ☺ Low-moderate cost and maintain  Microsoft Project  Vulcan Project Wiki  .NET Framework  Version One  B.L.S.  J2EE, …  Microsoft  RPI map  Ruby on rails SharePoint 74
  • 54. Conclusions  Semantic MediaWiki+ (http://smwforum.ontoprise.com) – Open-source, growing semantic wiki software system – Wiki-style text + semantic markups – Collaborative, user-governed subject models and data curation – Simple and extensible data models with easy import/export  SMW+ has many government and industry users – People built applications with it Knowledge Management via KB Effort (cost, people,…)  Now crowds can work – A way to leverage and exploit web-collected data Vulcan – A lightweight collaborative knowledge management tool Future  A new platform for lightweight KB size (number of assertions, complexity…) web application development 79
  • 56. (End of Slides) Backups starts here 81
  • 57. Case Study: Battle-space Luminary System  Discover when New Information represents a change in understanding of entities – Discovery of explicit entity links, implicit relationships  Large Volumes of Data in various formats – Unstructured news articles – Tactical Reports, Field Intelligence – Structured Database Information  Use Wiki Pages to represent current knowledge about an entity – “what we know”  Domain Ontology to represent domain of information – “what we want to know”  Issue Alerts when Significant Events occur – New information according to category – Changing information on topics of interest – Need to send information to various devices – cell phones, email, etc. 82
  • 58. System Design  Wiki Configuration – Semantic MediaWiki: Large developer community, active development, open source. Wikipedia uses MediaWiki, so scalability and performance are important. – Semantic Results Format: Provides various rich media displays of semantic information, including graphs, timelines, maps – Semantic Forms: Provides convenient user interface for entering semantic data into wiki, avoiding cumbersome wikitext – Semantic Notifications: Enables sending of notifications when results of semantic query change.  Domain Ontology – Created OWL Ontology for Terrorism  Semantic Parsing, Extraction, Reasoning – Java Process using various Open-Source Toolkits – Rapid plugin of new technologies 83 – Multiple Data Sources supported
  • 60. Wiki Content Design  Use Templates to Ensure Consistent Look-and-Feel – Templates Correspond to Ontology Classes – Fields within Templates correspond to Properties within Ontology – Rich Content Visualizations derived in consistent way  Hierarchical Categories match Class Hierarchy within Ontology – Ensures Validity for Properties – Category included on each Template page to ensure consistency  Forms Provide ability for users to enter data directly into wiki without knowing Wiki Text – Each form corresponds to a Template – Fields within forms correspond to the fields/properties within the Template – GUI can include auto-completion – Created Page immediately linked semantically to rest of Wiki 85
  • 62. Wikipedia for Porsches (Acceleration Data Example)  Information Need: All Porsche models that accelerate 0- 100kph in under 5, 6, and 7 seconds
  • 63. More Porsche Acceleration Data in Wikipedia
  • 64. Ultrapedia Main Page Main Page
  • 65. Semantics for Improved Wiki Navigation Tree View Control Abstract/Summary quick preview
  • 66. The Porsche 996 Acceleration Table In Ultrapedia
  • 67. Same Table as a Query
  • 68. Dynamically-Generated Tables forfast? Which Porsches accelerate Queries  Information Need: All Porsche models that accelerate 0- 100kph in under 5, 6, and 7 seconds
  • 69. Graph Views of the Acceleration Data
  • 70. External Data via a Live Ebay Query
  • 71. Linking to External Ebay Data
  • 72. Photos in Mercedes-Benz E-class W212 Gallery Section Wiki Articles as Data
  • 73. Timelines from Data Production Timeline View Volkswagen
  • 75. Editing Wiki Data In Place Return

Editor's Notes

  1. Of course once you have data, Ultrapedia can support data visualizations. This is a simple Flash-based chart widget based on the same Porsche 996 data, and included in Ultrapedia’s Porsche 996 page.It shows us that while acceleration varies dramatically, top speed and peak engine power remain fairly constant across models.The chart was specified manually with a query. There are of course a huge number of possible ways to chart a set of data, and most of these ways are uninteresting.In the Ultrapedia concept, we rely on article authors to specify interesting charts for their readers that will support the particular points in the article.
  2. But, did you know that Uusikaupunki, Finland, is a major hub for Porsche manufacturing?Ultrapedia allows us to drill down to look at Finland’s contribution to Porsche production.
  3. The problem we are going to solve is “find the 0-60 times of all Porsche cars in Wikipedia”This is a sample Wikipedia page for the Porshe 996, showing its acceleration times in a performance data table.This table is manually built – all the table data exists as constants in the table.
  4. This is a Wikipedia page showing 0-60 times for the Porsche Cayenne.If we have to manually go through every Porsche model to assemble the 0-60 data for each model and type, this is going to take a while.A better idea is to treat Wikipedia like a database, and simply query it. Enter Ultrapedia.
  5. This is the Ultrapedia home page.
  6. First notice that Ultrapedia can leverage all the data it extracts from Wikipedia to support a much more helpful UI.For example, Ultrapedia adds a manufacturer-based navigation system on the side, and show explanatory popups. These kinds of UI tweaks aren’t possible with MediaWiki now, and are an important benefit of having the semantic data.
  7. Remember that we want to find the 0-60 acceleration data for all Porsche models that Wikipedia knows about.Let’s start by looking at a query generated table on the Ultrapedia Porsche 996 page. For comparison, Ultrapedia also includes the original performance table from Wikipedia (above)
  8. This is Ultrapedia’sPorsche 996 performance table, built by a query to the Ultrapedia database of Wikipedia-extracted data.Notice that it has the same information that the original static table has, this is because we scrape the data from the static table.This table is dyamically generated at each page load out of the extracted Wikipedia data, so it is always up to date.It is sortable and also accepts feedback and ratings on individual data items.
  9. Now we can answer our question about 0-60 times across all Porsche models with one simple query in Ultrapedia. We can make this an Ultrapedia-only page – the page itself just 5 queries on it (one for each acceleration range).We could also do this as one big table but it’s easier to read as 5 smaller tables.All the data here flows from Wikipedia.
  10. Of course once you have data, Ultrapedia can support data visualizations. This is a simple Flash-based chart widget based on the same Porsche 996 data, and included in Ultrapedia’s Porsche 996 page.It shows us that while acceleration varies dramatically, top speed and peak engine power remain fairly constant across models.The chart was specified manually with a query. There are of course a huge number of possible ways to chart a set of data, and most of these ways are uninteresting.In the Ultrapedia concept, we rely on article authors to specify interesting charts for their readers that will support the particular points in the article.
  11. We can also use the data to dynamically link to other data sources. In this case we have configured the Ultrapedia Porsche 996 article to include a live ebay query to find out what the Porsche 996 sells for today…We access the ebay data through a web services interface.We can do this for arbitrary other web-service-accessible data sources, like amazon or geonames.In a government or enterprise context, we would link articles to supporting data from appropriate systems of record.
  12. I don’t think I’ll be buying one… I think I’d rather send my daughter to college.
  13. Pictures automatically get metadata, so Ultrapedia can deliver an iPod-like “cover flow” browsing experience with images to augment the table data. We could also embed images or videos in the tables.
  14. Since Ultrapedia includes some simple internal logic about time, we can generate simple browsable timelines and use them in articles.Here we see a timeline of VW models.
  15. But, did you know that Uusikaupunki, Finland, is a major hub for Porsche manufacturing?Ultrapedia allows us to drill down to look at Finland’s contribution to Porsche production.