A tech talk to Mi

1. Semantic Wikis
and Applications
Social Semantic Web In Action
Jesse Wang
2011.12.09
Tech Talk at Microsoft Research Asia

2. About Me: Jesse Wang 王嘉欣
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3. Who is Vulcan
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4. What does Vulcan do
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5. Paul Allen | Idea Man

6. It all began with an idea…
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7. Now the Idea Continues as Project Halo
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8. 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
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9. Crowdsourcing for Better Knowledge Acquisition
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10. Success of Wikis
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11. A Key Feature of Wiki
This distinguishes wikis from other publication tools
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12. 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
14

13. 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
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14. How about Deep Info?
Wikipedia has articles about…
• … all cities with info on their
populations, locations and
skyscrapers, etc.
… all German cars with engine
size, accelerating data…
Can you find:
Skyscrapers with 50+ floors
and built after 2000 in
Shanghai (or Chinese cities
with 1,000,000+ people)?
Or German(Porsche) cars that
accelerate from 0-100km/h in
5 seconds?
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15. Can Search Solve the Problem?
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16. How Wikipedia Answers – List!
http://en.wikipedia.org/wiki/List_of_fastest_
cars_by_acceleration
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17. Going Deeper
http://en.wikipedia.org/wiki/List_of_German_cars
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18. Deeper…
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19. And Deeper…
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20. And Now…
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21. To Get the Answer
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22. Look into List in Wikipedia
http://en.wikipedia.org/wiki/List_of_German_cars
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23. Editing Standard Wiki Article – Static List
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24. Static List, Tables, …, Not Useable Enough
http://en.wikipedia.org/wiki/List_of_lists_about_Oregon
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25. To Find More Info
• 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
• 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…
• And many more
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26. 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
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27. Two Perspectives
Wikis for
Metadata
Metadata for
Wikis
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28. Characteristics of Semantic Wikis
Semantic Wikis
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29. 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)
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30. What is the Promise of Semantic Wikis?
 Semantic Wikis facilitate
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
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31. 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
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32. 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
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33. Short History of Semantic MediaWiki (SMW)
 Born at AIFB
– Typed links and types and more
– Export articles as RDF
– Maximally flexible for the wiki user
 SMW 0.1 released by AIFB in Sept 2005
– Parser/storage support for typed links – [[type::link | label]]
– FactBox for semantic relations at end of article
– Special:SearchSemantic, with basic auto-completion for link types
– Simple query language (“ask”)
 Vulcan kicks off Halo Extensions to SMW project in August 2007
 SMW 1.0 released by AIFB in Dec 2007, Ontoprise releases Halo
Extension 1.0 in parallel
– “Property” instead of “Relation” and “Attribute”
– Many new datatypes/special pages/UI features
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34. Overview of Semantic MediaWiki (SMW)
 Open source (GPL)
– Well documented, active user forum
 Active development
– Commercial support (SMW+) available
 World-wide community
– International Conferences
• Next SMWCon on 4/25-27, 2012 in Carlsbad, CA
Very stable core, various extensions
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35. Semantic MediaWiki (SMW) Markup Syntax
Tsinghua is a university located in
[[Has location::Beijing]], with
[[Has population::27000|about 27 thousands]]
students.
In page "Property:Has location": In page "Property:Has population":
[[Has type::Page]] [[Has type::number]]
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36. Special Properties
 “Has Type” is a pre-defined “special” property for meta-
data
– Example: [[Has type::String]]
 “Allowed Values” is another special property
– [[Allows value::Low]],
– [[Allows value::Medium]],
– [[Allows value::High]]
 In Halo Extensions, there are domain and range support
– RDFs expressivity
– Semantic Gardening extension also supports “Cardinality”
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37. Define Classes
Beijing is a city in [[Has
country::China]], with population
[[Has population::2,200,000]].
[[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
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38. 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]]
…
}}
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39. SMW Extensions – Help Build Great Things
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
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40. Simple Example: Semantic Sci-Fi Movie Wiki
 Demo
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41. Example: Ultrapedia – Semantic Wikipedia
 Ultrapedia: An SMW demo built to explore general
knowledge acquisition in a wiki
 Wikipedia merged with the power of a database
 Help Readers and Writers Be More Productive
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42. Standard View of the Wiki Data
http://wiking.vulcan.com/up/index.php/Porsche_996

43. Dynamic View of the Acceleration Data

44. Graph View of the Acceleration Data

45. Dynamic Mapping and Charting

46. Information Discovery via Visualization
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47. 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
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48. Semantic Seahawks Football Wiki
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49. 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

50. The Inspiration
 We started with a
 We could have an
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51. Case Study 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
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52. Vulcan Project Management Wiki (Story)

53. Vulcan Project Management Wiki (Task)
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54. Vulcan Project Management Wiki (Visualizations)
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55. Screenshot of a Sprint page
Data automatically generated via template queries on page
http://wiking.vulcan.com/dev/index.php/Sprint_101020
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56. 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.
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57. 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
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58. SMW:: powerful tools and contents
Semantic MediaWiki and related extensions have more potential power

59. Need Release :: The Power
 Be used by more
people
 Content in more
places
 Accessible via more
applications
 Enhanced with more
semantics

60. Need :: Workflow Integration + Usability Enhancements
 Infrequent Wiki users frequently forget where the wiki
pages are located
 Search is a break from current workflow
 Search result can be noisy or irrelevant
 Usability:
– Wiki/Template/SF markup syntax is not extremely hard, but
enough to turn off many users
– To locate and consume info in SMW is just not easy
enough, need something better
 Why don’t we leverage Microsoft Office suite?

61. Microsoft Office :: The Most Popular Productivity Suite
500m users worldwide
>90% market share
Users live in the “suite”
Outlook always open
Potential for SMW

62. MICROSOFT OFFICE CONNECTOR :: How It Works
 Leverage Microsoft Office
Add-ins technology
 Bring SMW info to Office
applications on-demand
 API for semantic data I/O
 Utilize semantics to
improve relevance
 Smart actions for
semantic properties

63. Backstage::Semantic Wiki Object Model
 Wiki Validation  To get page info
 Authentication  Get all forms related info
 To get the categories  Edit and save page w/ form
– And descriptions  Change a property
 To get the article titles  Set form of a page
 To get the semantic  Create form templates
properties  To upload into the Wiki
http://wiking.vulcan.com/dev/index.php/SMW_Webservice_APIs

64. Microsoft Office Connector Smart Connections
• Consume relevant, targeted information
– With the tools you are already familiar with
– In the context – better relevance and productivity
– In place – no search overhead to break workflow
– In real time – data from wiki is live
– Automatically – linking to wiki
• Let you contribute to Wiki
– Without knowing where the content is
– Without learning wiki/template syntax

65. Openness of SMW as a Platform
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66. Semantic MediaWiki Enables Collaboration
Create and Manage Real
Knowledge
Build Social Semantic
Web Applications
In an Efficient and Cost-
Effective Way
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67. Acknowledgement
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68. (End of Slides)
Backups start here
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69. 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.
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70. 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
89 – Multiple Data Sources supported

71. Sample Content Page
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72. 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
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73. Sample Visualizations
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74. Wikipedia for Porsches (Acceleration Data Example)
 Information Need: All Porsche models that accelerate 0-
100kph in under 5, 6, and 7 seconds

75. More Porsche Acceleration Data in Wikipedia

76. Ultrapedia Main Page
Main Page

77. Semantics for Improved Wiki Navigation
Tree View Control Abstract/Summary quick preview

78. The Porsche 996 Acceleration Table In Ultrapedia

79. Same Table as a Query

80. Dynamically-Generated Tables forfast?
Which Porsches accelerate
Queries
 Information Need: All Porsche models that accelerate 0-
100kph in under 5, 6, and 7 seconds

81. Graph Views of the Acceleration Data

82. External Data via a Live Ebay Query

83. Linking to External Ebay Data

84. Photos in Mercedes-Benz E-class W212 Gallery Section
Wiki Articles as Data

85. Timelines from Data Production Timeline View
Volkswagen

86. Dynamic Mapping and Charting

87. Editing Wiki Data In Place
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