tutorial semantic wikis and applications

1.Tutorialon Semantic Wikis and Applications
Mark Greaves
Vulcan Inc.
[email protected]
Daniel Hansch
Ontoprise GmbH
[email protected]
Denny Vrandecic
Karlsruhe Institue of Technology
[email protected]
Jesse Wang
Vulcan Inc.
[email protected]

2. 2
Outline
Tutorial Introduction and Structure (Mark)
Introduction to Semantic MediaWiki (Denny)
Dive into Semantic MediaWiki (Denny)
Applications for Semantic Wikis (Mark)
Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
Connecting Semantic MediaWiki with MS Office (Jesse)
Augmenting Semantic MediaWiki with a Triple Store (Daniel)
Future Development (Denny, Daniel, Jesse)
Wrap Up and Q&A (Mark)
Break (30 mins)
3. 3
Outline
Break (30 mins)
4. Context:Social Web, Semantic Web, and Semantic Wikis
4
SoftwareAgents
Expert Systems
Freebase
Schema Integration
Facebook
OpenGraph
Linked Data
Ontologies
SemanticWikis
Semantic
Desktops
Evri
Thesauri
Twine/T2
Prediction Markets
Increasing Data Interconnection
PIMs
Ning
Databases
FaceBook
SearchEngines
Amazon Reviews
Content Portals
Web sites
Wikipedia
File servers
Blogs
Twitter
Increasing Social Interconnection
Based on a diagram by Nova Spivak, Radar Networks
5. A Range of Semantic Wiki Platforms
KiWi Knowledge in a Wiki
Knoodl Semantic Collaboration tool and application platform
Freebase- Collaborative platform for almanac data by Metaweb
OntoWiki
PhpWiki
Semantic MediaWiki- an extension toMediaWikithat turns it into a semantic wiki (and SMW extensions)
TikiWiki- CMS/Groupware integratesSemantic linksas a core feature
Wikidsmart- adds semantics to Confluence (from zAgile)
5
5
6. 6
Outline
Break (30 mins)
7. Semantic MediaWiki
Denny Vrandei, KIT / ISI, USC
June 22 2010, San Francisco
8. Wikis are great
Enable new scale of human collaboration
Everyone can read
Everyone can write
Everyone gets aggregated
Everyone is accountable for everything
But some things are better left to machines
8
9. edit
wow. I can change the web.
lets write an encycolpedia!
10. 11. Wiki Clock
http://pageoftext.com/wikiclock
12. Wikis are great
Enable new scale of human collaboration
Everyone can read
Everyone can write
Everyone gets aggregated
Everyone is accountable for everything
But how are semantic wikis different?
Semantic
+ computer
v
12
13. edit
edit
edit
Country
City
Population = 745,514
Area = 39 km2
capital
mayor
edit
edit
Birthdate =
1 April 1946
14. edit
edit
edit
edit
edit
edit
May 27 1994, Tim Berners-Lee, Keynote at WWW1
15. edit
edit
16. What humans are good at
What machines are good at
Understanding
Why
Tacit knowledge
Stories
Following hunches
Checking external refs
Executing
Facts and figures
Explicit knowledge
Keeping track and logs
Analyzing big style
Calling web services
17. 18. Universal Access to
All Knowledge
19. 19
What Wikipedia knows
Wikipedia has articles about
all cities
their populations
their mayors
So can I ask for a list of the worlds ten largest cities with a female mayor?
20. 20
Lets see what happens
21. Wikipedias answer: lists
21
22. 23. 24. 25. 26. 26
27. 27
28. 28
29. 29
30. 30
31. 32. 32
33. 34. Computers are stupid
34
35. 35
What humans see
36. What humans see
Karlsruhe
... has a population of 285,812
... is located inGermany
... was founded in 1715
... has mayor Heinz Fenrich
36
37. What computers see
38. What computers see
Karlsruhe
... 285,812
... Germany
... 1715
... Heinz Fenrich
38
39. Computers dont make connections
39
40. Computers need our help
40
41. Karlsruhe
Karlsruhe is a city in
[[Germany]].
[[Country::Germany]].
Germany
Country
Karlsruhe
Country
Germany
Karlsruhe
Mayor
Heinz Fenrich
Heinz Fenrich
Gender
Male
41
42. 43. {{#ask:
[[Category:City]]
[[located in::
Baden-Wrttermberg]]
| format=barchart
| ?population
}}
44. External data reuse
Export formats
RDF/XML
SPARQL
RDFa
CSV
JSON
iCal
vCard
Bibtex
44
45. 46. 47. 47
Outline
Break (30 mins)
48. External data reuse
Computer understands wiki content
Knowledge based applications
A number of export formats
RDF/XML, SPARQL, RDFa, CSV, JSON, iCal, vCard, Bibtex, ...
RDF APIs in programming languages
Java, JavaScript, C/C++, Python, Ruby, Haskell, .Net, PHP, Common Lisp, Prolog,
Standards based
URIs, XML, RDF, OWL, SPARQL,
49. importSemanticMediaWikias smw
wiki = smw.SMW("http://semanticweb.org/")
denny = wiki.load(DennyVrandecic")
printdenny.affiliation
50. Test wiki
Go to http://scratchpat.referata.com
Click on log in and then on Create an account
Suggestion: use your name as your login
Enter your eMail (for forgotten passwords)
51. Editing the wiki
Go to your own page (page with your name)
Click on edit
Try to add or change text
You can cancel anytime, preview (just for you), or save the changes so that everyone can see them
52. Quick overview of wiki markup
'''three apostrophes''' will make text bold
''two apostrophes''' will make text italic
[[Text in double square brackets]] will be links to the page named as the text in the brackets
[[Link target|link text]] will display a link that looks like link textbut links to link target
The wiki is case sensitve but not on the first letter of a link
The wiki is Unicode
53. Slide 53
Overview of semantic markup
To add a page P to category C type [[Category:C]] on page P
To make a typed link of type R from page P1 to page P2 type [[R::P2]] on page P1
To state the value V of an attribute A on page P type [[A::V]] on page P
Example:
54. Data values and types
Attributes like [[birthdate::February 27 1978]] or [[population::3,635,389]] must know the type of the value
This is done by adding [[has type::T]] on the page of the attribute
Available, predefined types:
Telephone number
Record
URL
Email
Annotation URI
Geographiccoordinate (S Maps)
Enumeration
Customunits
Page
String
Number
Boolean
Date
Text
Code
Temperature
55. Add your own information
Now add information about yourself
For example: nationality, affiliation, age, birthday, hair color, likes
Save or preview to see if and how the information has been understood
Blue links mean there is a page about it
Red link means there is no page about it
56. Collaborative ontology engineering
There are pages describing categories and properties
Informal description
Can be discussed
Can be edited
Extensional descript.
List of all instances
But: only direct ones
Supercategories
57. Slide 57
Social aspects
Task: come up with a vocabulary and the relation between the vocabularies for the whole group, using the wiki
How to decide which properties and categories are important?
How to define the properties or categories?
How to ensure high quality data? What does it mean?
How to control the wiki knowledge base and its growth?
Browse the wiki to see the results and connections
58. Querying the knowledge
Go to Special:Ask
Enter a query
Queries look like this:
Conditions on a category: [[Category:X]]
Conditions on a property: [[R::X]]
Property conditions can be ranges, [[R::>X]], [[R::Property conditions: any value [[R::+]]
Print statements: ?R
Examples follow
See also online docs
59. Query examples
[[population::>1,000,000]] anything with a population of over a Million
[[located in::Korea]] anything that is located in Korea
[[affiliation::+]] anything that has any stated affiliation
[[Category:Tutor]] all tutors
[[Category:Tutor||Student]] all tutors or students (logical or)
[[Category:Tutor]] [[Category:Student]] everyone who is both
60. Querying and social aspects
Querying can only be done on aligned vocabularies
If half of the people use affiliation and the other half works for you cannot query the knowledge easily
Inside SMW, information integration usually happens with social tools, not with technology
Gardening tools can help with aligning vocabularies, but not replace them
Tools that allow you to rename a property throughout the wiki
Or to join two different names
61. Querying the wiki
{{#ask:
[[Category:City]]
[[Mayor.Gender::Female]]
| sort=Population
}}
{{#ask:
[[Category:Country]]
[[Continent::North America]]
|?Population
}}
63. Result rendering
{{#ask:
|?Population
|format=piechart
}}
65. Pie chart
{{#ask:
|?Population
|format=barchart
}}
67. Bar chart
68. 68
Outline
Break (30 mins)
69. SNPedia
70. HL7 Healthcare Terminology Management
70
71. Taaable
71
72. Chickipedia
72
73. Football Indexing Wiki

Non-Wikipedia Look/Feel

74. Play-by-play video search 75. Highlight reel generation 76. Search on crowd-defined patterns (touchdowns with big hits) 77. Tree-based navigation widget

tutorial semantic wikis and applications

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