A talk at UMass Lowell

1. Semantic Web:
In Quest for the Next
Generation Killer Apps
Jie Bao
Tetherless World Constellation
Rensselaer Polytechnic Institute
baojie@cs.rpi.edu
http://www.cs.rpi.edu/~baojie
Oct 22nd, 2010 @ UMass Lowell 1

2. Outline
• Why Semantic Web?
• Key SW Standards
• Opening Data for SW
• Building SW Applications
2

3. What is a Killer App ?
3

4. A Killer App is
• …Any computer program that is so
necessary or desirable that it proves
the core value of some larger
technology, […]. A killer app can
substantially increase sales of the
platform on which it runs.
-- Wikipedia
4
http://en.wikipedia.org/wiki/Killer_application

5. Visicalc
5

6. Excel
6

7. Netscape
7

8. Facebook
8

9. Web Itself
9
Sir Tim Berners-Lee
As an app of the Internet
Picture Source: http://commons.wikimedia.org/wiki/File:InternetProtocolStack.png

10. ATM
10
A killer app of database and network technologies
Picture source: http://en.wikipedia.org/wiki/File:ATM_750x1300.jpg

11. Where are the killer apps
for the Semantic Web?
11

12. What is Semantic Web?
12

13. Web of Documents
13

14. from a
Web of Documents
to a
Web of Data
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15. Web of Data
15
Travel Data

16. Web of Data
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Financial Data

17. Web of Data
17
Housing Data

18. But data integration is difficult
18

19. and is often ad-hoc
19

20. and there are other issues
20

21. Inconsistency
21

22. Inconsistency
22

23. Inconsistency
23

24. Different Naming
24
#iswc2010 is #iswc

25. Different Naming
25

26. Inference
26
From: hotwire.com

27. Inference
27
From: travelocity.com
Can this be
automated?
(Li Ding teaches me this trick)

28. What We Need
• A standard data interchange format
• A standard representation of the
meaning of data
• A standard way to link data
28

29. Semantic Web Languages
29
Source: W3C's Semantic Web Activity / Semantic Web overview
http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/#%2824%29
The Layer Cake

30. RDF = Resource Description
Framework
30
affiliation
affiliation
knows

31. RDF
31
swrc:affiliation
swrc:affiliation
foaf:knows
<http://www.cs.rpi.edu/~baojie>
<http://www.rpi.edu>
< http://www.cs.rpi.edu/~hendler>
swrc:=http://swrc.ontoware.org/ontology#
foaf:=http://xmlns.com/foaf/0.1/

32. RDF
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix swrc: <http://swrc.ontoware.org/ontology#> .
@prefix foaf: <http://xmlns.com/foaf/0.1> .
@prefix rpics: < http://www.cs.rpi.edu/~> .
rpics:baojie swrc:affiliation <http://www.rpi.edu> .
rpics:baojie foaf:knows rpics:hendler .
rpics:hendler swrc:affiliation <http://www.rpi.edu> .
32

33. SPARQL= SPARQL Protocol And RDF
Query Language
A query language for RDF
SELECT ?person ?org
WHERE{
?person foaf:knows rpics:hendler .
?person swrc:affiliation ?org .
}
(find all people (and their affiliations) who know
Hendler)
33

34. OWL = Web Ontology
Language
• A much more powerful ontology language
• Examples (informally)
– If twitter:baojie and linkedin:baojie both use email
baojie@cs.rpi.edu, then they belong to the same
person.
– If Westin hotel is in Palo Alto, and Palo Alto is in
the Bay Area, then Westin hotel is in the Bay Area.
• OWL 1 (2004), OWL 2 (2009)
34

35. How they work in the real world?
35

36. You may not be aware that there are
already plenty of semantic data around.
36

37. BestBuy
37
http://www.bestbuy.com/site/The+Matrix+-+DVD/9316073.p?id=29857&skuId=9316073&st=9316073&lp=1&cp=1

38. BestBuy (GoodRelations)
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http://products.semweb.bestbuy.com/products/9316073/semanticweb.rdf

39. Facebook
39

40. Facebook (Open Graph)
40

41. LinkedIn
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http://www.linkedin.com/in/jiebao

42. LinkedIn (Microformat)
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http://microformatique.com/optimus/?uri=http://www.linkedin.com/in/jiebao

43. SlideShare
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http://www.slideshare.net/baojie_iowa/semantic-history-towards-modeling-and-publishing-changes-of-online-semantic-data

44. SlideShare (RDFa)
44
http://www.w3.org/2007/08/pyRdfa/extract?uri=http://www.slideshare.net/baojie_iowa/semantic-history-towards-modeling-and-publishing-
changes-of-online-semantic-data

45. IMDB
45
http://www.imdb.com/name/nm0000125/

46. IMDB (OG+Microformat)
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http://www.w3.org/2007/08/pyRdfa/extract?uri=http://www.imdb.com/name/nm0000125/

47. Sig.ma (Data Aggregation)
47
http://sig.ma/search?q=Jie+Bao

48. Sig.ma
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http://sig.ma/search?q=Jie+Bao

49. Data From Spreadsheet
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Dominic DiFranzo and Li Ding - http://data-gov.tw.rpi.edu/wiki/Demo:_White_House_Visitor_Search

50. Data From Spreadsheet
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6.46 billion RDF triples now.

51. Data From Relational DB
51
http://demo.openlinksw.com/about/html/http/demo.openlinksw.com/Northwind/Customer/ALFKI

52. 52
Many Many More

53. Then, how semantic data help us to build
(killer) apps?
53

54. Semantic Twitter
54
Joshua Shinavier, TwitLogic SPARQL widget

55. Financial Data
55
Perry Grossman, Devin Mcqueeney, Graham G Rong, Lorin Wilde, Danny Yuan, Jie Bao
(coach). FinanceSphere, MIT LinkedData IAP 2010 Project

56. Financial Data
56
Bao, J., Rong, G., Li, X., and Ding, L. Representing Financial Reports on the Semantic Web - A
Faithful Translation from XBRL to OWL. In The 4th International Web Rule Symposium (RuleML).
2010
XBRL= eXtensible Business Reporting Language

57. RPI Map
http://map.rpi.edu
Jie Bao , Jin Guang Zheng, Rui Huang & Li Ding. Mesh-up Map and Events on Semantic Wiki: Applications in e-Science and Campus Information
System. SemanticWiki mini-series Session-4. Jan. 22, 2009. ontolog.cim3.net

58. Semantic Email
58

59. Data-gov Wiki
Li Ding and James Michaelis and Deborah L. McGuinness and Jim Hendler, Making Sense of
Open Government Data, in Proceedings of WebSci2010, 2010.

60. Data Mashup and Visualization
60
DiFranzo, D.: Developer Diary: CASTNET Ozone Map Demo. In WebSci 2010 poster.
http://data-gov.tw.rpi.edu/wiki/Demo:_Clean_Air_Status_and_Trends_-_Ozone

61. Data Mashup and Visualization
61
James Michaelis: http://data-gov.tw.rpi.edu/wiki/Demo:_Comparing_US-USAID_and_UK-DFID_Global_Foreign_Aid

62. Where are we?
62

63. Where are we?
63
Picture source: Wikipedia (http://en.wikipedia.org/wiki/Technology_adoption_lifecycle)
Adapted from “Semantic Web Adoption and Applications”, Ivan Herman, W3C. 2010-10-07 Slide 5
2005

64. Where are we?
64
Picture source: Wikipedia (http://en.wikipedia.org/wiki/Technology_adoption_lifecycle)
Adapted from “Semantic Web Adoption and Applications”, Ivan Herman, W3C. 2010-10-07 Slide 5
2010

65. The 2007 Gartner predictions
• By 2012, 80% of public Web sites will use some
level of semantic hypertext to create SW documents
[…] 15% of public Web sites will use more extensive
Semantic Web-based ontologies to create semantic
databases
• By 2017, we expect the vision of the Semantic Web
[…] to coalesce […] and the majority of Web pages
are decorated with some form of semantic hypertext.
65
“Finding and Exploiting Value in Semantic Web Technologies on the Web”, Gartner Report, May 2007

66. Imagine a world where data are linked
and make sense
66

67. On ATM
67
You can check transactions by their categories, and the ATM knows that not all items
from BestBuy are electronics (e.g., office supplies), since it is connected to the BestBuy
product database.

68. On TV
68
It can generate a personalized program list of movies starred by Sean Connery,
since it is connected to IMDB

69. In Your Car
69
Picture from: http://electronics.howstuffworks.com/gadgets/automotive/car-gps-accidents.htm/printable
The in-car GPS tells you attractions of the revolutionary war era, since it can read a semantic
version of Wikipedia and a geo-location database from the US government.

70. We are only limited by our imaginations
70

71. That’s why I believe the Semantic Web is
a beautiful thing
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72. Thank you!
Slides are available @
http://slidesha.re/aimhRY
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