This document discusses semantic wikis and Project Halo. It provides an overview of semantic wikis, what they are, how they work, and examples of semantic wiki software. It then discusses Project Halo, its goals of addressing problems with knowledge bases, focus areas including AURA and SILK, and using wikis and crowdsourcing for knowledge acquisition.

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

3. Who is Vulcan
3

4. What does Vulcan do
4

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

13. Success of Wikis
14

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

31. System Architecture
39

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

37. Dynamic Mapping and Charting

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

40. Semantic Seahawks Football Wiki
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41. Based on Simple Templates and Forms
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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

48. Vulcan Project Management Wiki (Story)

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
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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

55. Acknowledgement
80

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

59. Sample Content Page
84

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

61. Sample Visualizations
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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

74. Dynamic Mapping and Charting

75. Editing Wiki Data In Place
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