Vocabulary construction is critical to the success of semantic technologies. Can we learn from communities where practical vocabularies have emerged?

1. The Social Fabric of Semantics
Jamie Taylor, Ph.D.

2. http://rdf.freebase.com/ns/en.jamie_taylor

5. Explicit Semantics in
Surprising Places

6. microformats
HTML5 MicroData
Open Graph Protocol
RDFa

12. We have overlooked the human “stack”

13. The Crisis of Vocabulary
Much formal analysis of
knowledge representation
Little guidance on what
actually works

14. education
nationality
contained-by
education
member-of
event
albums
label
contained-by
contains
member-of
The arrangement of
entities in a graph is
not predetermined
by a higher being

15. contains
contained-by
event
member-of
nationality
education
albums
Vocabulary is a social process

16. Semantics: To communicate
meaning, resulting in an action
Or at least so Blue Guy can write
code that responds to the graph in a
way consistent with Red Guy's
expectations

18. Vocabulary
"All the types of things you
can say about something"

19. http://rdf.freebase.com/ns/en.paul_david_hewson

20. Alison Hewson
EDUN
Mount Temple
Comprehensive School
May 10, 1960
U2
Million Dollar Hotel
End of Violence
Elevation Partners
Show 8
Dublin
spouse
date of birth
founder
performer
education
founder
producerperform
er
bornin
memberof
Semantics are in the Links

21. Alison Hewson
EDUN
Mount Temple
Comprehensive School
May 10, 1960
U2
Million Dollar Hotel
End of Violence
Elevation Partners
Show 8
Dublin
spouse
date of birth
founder
performer
education
founder
producerperform
er
bornin
memberof
Semantics are in the Links

22. Do you
understand the words
that are coming out of
my mouth?

23. The Twitter Vocabulary
@
#
Short URLs

24. Pivot on @

25. Pivot on Short URL

26. Pivot on #

27. #

28. Broadcast: U(n) = n

29. Telephone:
Metcalfe's Law
U(n) = n2

30. Group Network Formation:
Reed's Law
U(n) = 2n

31. Reed's Law
N
Value
N
N^2
2^N

32. N
Value
N N2
2N
Broadcast Email Chatrooms

33. N
Value
N N2
2N
Tweets #tagsFeeds
#tags are a USER invention!

34. N
Value
N N2
2N
Folksonomy ???Ontology

35. Twannotations
Tweets have "type"
Name/Value Structure
What's the vocabulary?
•Anything you want
•Lead by example

36. Vocabulary and Visibility
Pros: Feedback, Incentive, Training, Convergence

37. Vocabulary and Visibility
Cons: Usage for side effects

38. Lessons from everyday vocabulary
Wikipedia Word Frequency
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
16000000
18000000
20000000
0 20 40 60 80 100 120
Rank
Frequency
Data from Victor S. Grishchenko

39. Zipf’s Law
!
Plot by Victor Grishchenko

40. Zipf’s Explanation
Law of Least Effort:
Use a few common words to communicate main concept
Use a few rare words to disambiguate concepts
Satisficing

42. 535,393 Categories
2k French Films

43. 17 films

45. Schema Principle #1
Use Types Liberally:
Use a few large, encompassing Types to
provide general information
Use several smaller, fine grained Types to
provide detailed information

47. The Freebase Commons
·American football ·Internet
·Anime/Manga ·Language
·Architecture ·Law
·Astronomy ·Library
·Automotive ·Location
·Aviation ·Martial Arts
·Awards ·Measurement Unit
·Baseball ·Media Common
·Basketball ·Medicine
·Bicycles ·Metaweb Types
·Biology ·Meteorology
·Boats ·Military
·Broadcast ·Music
·Business ·Olympics
·Celebrities ·Opera
·Chemistry ·Organization
·Comics ·People
·Common ·Geography
·Computers ·Projects
·Conferences ·Protected Places
·Cricket ·Publishing
·Data World ·Radio
·Digicams ·Rail
·Education ·Religion
·Engineering ·Royalty
·Event ·Soccer
·Clothing and Textiles ·Spaceflight
·Fictional Universes ·Sports
·Film ·Symbols
·Food & Drink ·Tennis
·Freebase ·Theater
·Games ·Time
·Geology ·Transportation
·Government ·Travel
·Hobbies and Interests ·TV
·Ice Hockey ·Video Games
·Influence ·Visual Art
Top-level domains
schema = vocabulary

48. Ontologies you design will be too complicated
because almost all people will use a small
subset of it
Ontologies you design will be too simple
because there will be a long tail of users who
will want to express something you didn’t cover
--Colin Evans (Metaweb)
Solution:
• Provide a core
• Let the community tune the specifics to their needs

49. What is a Politician?

50. Schema Principle #2
Avoid Types which "carve out"
categories of things

51. "Original TV Program"
• Is a TV Program
• Isn't an adaptation of a film
• Isn't an adaptation of a book
• Isn't an adaptation of a play
• Wasn't spun off from another TV Program
• Hasn't spun off any other TV Programs

54. "Original TV Program"
[{
"name": null,
"type": "/tv/tv_program",
"b:type": {
"id": "/media_common/adaptation",
"optional": "forbidden"
},
"spun_off_from": [{
"id": null,
"optional": "forbidden"
}],
"spin_offs": [{
"id": null,
"optional": "forbidden"
}]
}]
Show as Two Views
not a MQL query

55. Principle #2 Corollary
Strive for bright lines between Types
• Let queries and simple types do the work
• Better, easier to maintain data quality

56. What are you sitting on?
Chair
Furniture
Folding Chair
Natural Category
Added
Features?
What does one
look like?
Eleanor Rosch

57. HTML5 MicroData
Open Graph Protocol
#

58. Addendum

59. Social Network Analysis
Resources
Wikipedia
Jon Kleinberg
http://www.cs.cornell.edu/home/kleinber
Twitter
Kwak et al. WWW2010
http://an.kaist.ac.kr/traces/WWW2010.html

60. Modeling Resources
McGuinness & Noy's Ontologies 101
Attend when possible!
http://ksl.stanford.edu/people/dlm/papers/ontology101
Toward Principles for the Design of
Ontologies Used for Knowledge Sharing
http://tomgruber.org/writing/onto-design.htm
Allemang & Hendler
Semantic Web for the Working Ontologist