Web 2.0 is not only about making sites easier for people to interact with, but it is also about creating webs of data that machines can also interact with. These slides looks at a few examples of technologies that can help weave the data web, and shows some example applications, with a focus on science.

1. Man
vs
Machine
Main theme, Web 2.0 is as much about machine consumable as human consumable data.

2. Web 1.0
Web 2.0
DoubleClick
Google AdSense
Ofoto
Flickr
Akamai
BitTorrent
mp3.com
Napster
Britannica Online
Wikipedia
personal websites
blogging
evite
upcoming.org and EVDB
domain name speculation
search engine optimization
page views
cost per click
screen scraping
web services
publishing participation
CMS wikis
directories (taxonomy)
tagging (folksonomy)
stickiness syndication
The meme of Web 2.0 was influenced by comparing pre-dot com bubble companies and post
dot com bubble companies.
What is the difference between the list on the left and the list on the right?
Let’s take the example of Brtiannica vs Wikipedia.
The information in Britannica is centrally controlled. It has a relatively small number of contributors.
The workload per contributor is high.
Wikipedia is open to anyone to contribute. A collaboration of 1000’s can lead to a work of equal
quality to
a more centrally controlled method.
Britannica’s revenues decreased from 650M to 50M over a 10 year period!
The new sites make it easy to add information and use that information to
answer or solve problems for people.

3. y
easy
contributing
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

4. y
easy
contributing
semantic web
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

5. y
easy
contributing plain text, emails
semantic web
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

6. y
easy plain text, emails hyperlinks
views
tags
citations?
contributing
semantic web
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

7. y
easy plain text, emails hyperlinks
views
tags
citations?
contributing
academic papers semantic web
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

8. y
easy plain text, emails hyperlinks
views
tags
citations?
contributing
microformats
MicroFormats
academic papers semantic web
hard mining easy
Two key parts to Web 2.0 are easy addition of information into
the system (user generated content), followed by ways of mining
that information.
One of the thesis that we are following by trying to work in this context
is that by realizing the nature of the flow of information
and the availability of ways of mining that information
we can create useful solutions to real problems.
Companies that find ways to do this should succeed.

9. The Kind of Information that we can capture with Connotea is typical of many sites.
For Connotea we have:
- citation information
- usage patterns, (when did an item get added to our DB, how many times has it been added)
- user generated meta-data such as tags
- Potentially social network information, how many of my friends have added this item?

10. The Kind of Information that we can capture with Connotea is typical of many sites.
For Connotea we have:
- citation information
- usage patterns, (when did an item get added to our DB, how many times has it been added)
- user generated meta-data such as tags
- Potentially social network information, how many of my friends have added this item?

11. The Kind of Information that we can capture with Connotea is typical of many sites.
For Connotea we have:
- citation information
- usage patterns, (when did an item get added to our DB, how many times has it been added)
- user generated meta-data such as tags
- Potentially social network information, how many of my friends have added this item?

12. The Kind of Information that we can capture with Connotea is typical of many sites.
For Connotea we have:
- citation information
- usage patterns, (when did an item get added to our DB, how many times has it been added)
- user generated meta-data such as tags
- Potentially social network information, how many of my friends have added this item?

13. The Kind of Information that we can capture with Connotea is typical of many sites.
For Connotea we have:
- citation information
- usage patterns, (when did an item get added to our DB, how many times has it been added)
- user generated meta-data such as tags
- Potentially social network information, how many of my friends have added this item?

14. Gatherin Trustin Integrat Analyz Triangl
g g ing ing es
del.icio.us
Many Web 2.0 sites, have created islands of data.
Some key technologies for bridging these islands include fire eagle, OpenId and OAuth.
- rfid, fire eagle point the way to merging these islands with the real world

15. Whats the process?
• Gathering The data
• Trusting the data
• Integration / Disambiguating
• Understanding and analyzing the data

16. DOI
Some key technologies for bridging these islands include fire eagle, OpenId and OAuth.
In the publishing world DOIʼs are a key technology

17. Internet
Cf
Site
or Internet Site
Application
OpenID cf OAuth
OpenID allows a single person to interact with multiple web sites using one log-in mechanisim
OAuth allows both desktop and web applications to share data using one authentication mechanisim

18. Rated 5/5 Rated 1/5
Redemption Based-on-Play
Android Love Refugee
Spacecraft
Time-Travel Soldier Famous-Score Hope
Alien
Blockbuster Alien Broken-Heart Blockbuster
Space
War
Futuristic Based-on-Novel Racism
Artificial-Intelligence Hero Melodrama
Once you merge the data, you have to understand it.
The tags that a person uses across different services can give you a more holistic picture of their interests

19. However tags can be ambiguous.
Some technologies that are addressing this a semantic web technologies, look at projects such as
Tagora http://www.tagora-project.eu/
DBpedia http://dbpedia.org/
SIOC http://sioc-project.org/
FOAF http://www.foaf-project.org/

20. Open
Science Web 2.0
Semantic
Web
Though not exactly the same, web 2.0, Open science and the semantic web work well together
and they share some common traits, namely sharing, openness and minability of information.

21. Growth in submissions to the arXiv, demonstrating growth in scientific output
certainly growth in output of available data online in e-format
There is some discussion about whether there is an information overload, as the main journals
are still the important ones, but reading habits have changed

22. Discussion Groups and Mailing lists contain a huge amount of information from
from snippets of computer code, to long discussions about topics.
Mark Mail, from MarkLogic, have a site that mines this information. Here we see
a comparison of a search for FORTRAN vs a search for Java.
At the moment these kinds of archives are mainly relevant in the computer science area, but
these kinds of conversations are going on all the time in every field.
http://markmail.org/

23. Amazon use page views and a database of user purchases to find things you might like.
Again, here they are using data that they get for free from people using their site.
Google page rank is another canonical example

24. Crystal Eye
Social/Knowledge
Networking
An example of two type of uses in science:
CrystalEye http://wwmm.ch.cam.ac.uk/crystaleye/
example bond length for a structure: http://wwmm.ch.cam.ac.uk/crystaleye/bondlengths/H-Rb.svg
Nature Network: human-human interaction

25. Nature Web Publishing
group
OTMI
The main products that we have developed so far are
- database gateways
- OTMI (open text mining interface)
- podcasts
- scintilla
- nature network
- nature preceedings
- connotea

26. There are also other tools out there that are doing the same kind of thing, but I’m partial.

27. There are also other tools out there that are doing the same kind of thing, but I’m partial.

28. There are also other tools out there that are doing the same kind of thing, but I’m partial.

29. There are also other tools out there that are doing the same kind of thing, but I’m partial.

30. There are also other tools out there that are doing the same kind of thing, but I’m partial.

31. There are also other tools out there that are doing the same kind of thing, but I’m partial.

32. Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

33. Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

34. Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

35. Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

36. Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

37. Repository
Repository
Repository
Repository
Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

38. Repository
Repository
Repository
Repository
Repository
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

39. Repository
Repository
Repository
Repository
Repository
Citation Pubmed Activity
Management Integration Listing
Discuss how social silo’s can be interchange locations between repositories
and also between repositories and applications that we might also be built on top
of the social silos.

40. Connotea citation parsing modules
This model was quick and easy to implement but using the URL as the unique key.

41. Amazon.pm DOI.pm LivingReviews.pm
PLoS.pm RIS.pm SpamDNSBL.pm
autodiscovery.pm
BibTeX.pm Dlib.pm NASA.pm
PMC.pm Scitation.pm Springer.pm
blog.pm
Blackwell.pm Highwire.pm NPG.pm
PNAS.pm Self.pm Wiley.pm
ePrints.pm
BmcPdf.pm Hubmed.pm OUP.pm
Pubmed.pm Simple.pm arXiv.pm
We have a bunch of citation modules
they currently have to be written in perl, and this is a problem,
there is nothing similar to the scaffold infrastructure that Zotero has

44. Title

45. Title

46. Title
Date

47. Title
Date

48. Title
Author Date

49. Title
Author Date

50. Title
Author Date
PMID/DOI

51. Getting data in, part 2
The meta-data from the paper has been captured
When you begin to add tags suggested tags are presented based on
tags you have already used
paper by Huberman et all shows that displaying all tags drives tag-onomies to stable state (Polya-
Renyi urn model)
You need to display the full community tags, which we don’t do ... yet.

52. Getting data in, part 2
The meta-data from the paper has been captured
When you begin to add tags suggested tags are presented based on
tags you have already used
paper by Huberman et all shows that displaying all tags drives tag-onomies to stable state (Polya-
Renyi urn model)
You need to display the full community tags, which we don’t do ... yet.

53. Getting data in, part 2
The meta-data from the paper has been captured
When you begin to add tags suggested tags are presented based on
tags you have already used
paper by Huberman et all shows that displaying all tags drives tag-onomies to stable state (Polya-
Renyi urn model)
You need to display the full community tags, which we don’t do ... yet.

54. user home page,
toolbox, on right
user tags
related tags
related users, groups

55. user home page,
toolbox, on right
user tags
related tags
related users, groups

56. user home page,
toolbox, on right
user tags
related tags
related users, groups

57. Getitng data out
Open Data, important
Export only gets out the citation data, and not extra meta data that the user
has added such as comments or tags.
Formats: txt, rdf, BibTex,RIS,EndNote an api??

58. Getitng data out
Open Data, important
Export only gets out the citation data, and not extra meta data that the user
has added such as comments or tags.
Formats: txt, rdf, BibTex,RIS,EndNote an api??

59. perl
mod_perl
Template Toolkit
MySQL
Open Source, GPL2.5 v 1.8.1
web1.75 application
Discuss reasons for OS, discuss web1.8.1
- hope for community involvement,
- Code is not MVC structured, this has led to some problems with adoption
- We do have some people running their own instances, with some feedback ,
but we would like to eventually make the code easier to work with
- Why not port it? That’s a big can of worms, and someone needs to convince me of
the benefits.
- If for some reason we choose to no longer support connotea then the data and the code could be
hosted be someone else,
- Someone asked me what do how do they know we don’t cheat, and preferentially
return NPG articles in searches, well the code is open so if you are that paranoid
you can go and run an instance yourself and check up on us.

60. http://www.connotea.org/user/IanMulvany
http://www.connotea.org/users/tag/scifoo
http://www.connotea.org/user/IanMulvany/tag/scifoo
http://www.connotea.org/user/IanMulvany/tag/science
http://www.connotea.org/user/IanMulvany/tag/
science2.0+citation
Example of calls to query the data, html output

61. http://www.connotea.org/data/user/IanMulvany
http://www.connotea.org/data/users/tag/scifoo
http://www.connotea.org/data/user/IanMulvany/tag/scifoo
http://www.connotea.org/data/user/IanMulvany/tag/
science
http://www.connotea.org/data/user/IanMulvany/tag/
science2.0+citation
Example of API calls
(you don’t have to type them in green when making the call)

62. http://www.connotea.org/rss/user/IanMulvany
http://www.connotea.org/rss/users/tag/scifoo
http://www.connotea.org/rss/user/IanMulvany/tag/scifoo
http://www.connotea.org/rss/user/IanMulvany/tag/science
http://www.connotea.org/rss/user/IanMulvany/tag/
science2.0+citation
Example of RSS calls
(you don’t have to type them in green when making the call)
We create an rss feed of everything

63. Thousands
Ja
n
100
200
300
400
500
600
0
-0
M 5
ar
-0
M 5
ay
-0
5
Ju
l-0
Se 5
Growth in Connotea bookmarks
p-
0
N 5
ov
-0
Ja 5
n-
0
Entries in All Libraries
M 6
ar
-0
M 6
ay
-0
6
Ju
l-0
Se 6
p-
0
N 6
ov
-0
Bookmark Growth in Connotea
Ja 6
n-
0
M 7
ar
-0
M 7
ay
-0
7
Ju
l-0
Se 7
p-
0
N 7
ov
-0
Ja 7
n-
0
M 8
ar
-0
8

64. Mirko Gontek at the university of Colonge
information visualization of links in connotea
These social links can create networks of information on top of the basic
information.
This is what we want to use to start building collaborative intelligence into
these systems.