This document demonstrates how to explore linked data by starting with a person's profile and pivoting to related data across multiple sources. It shows finding a person on Razorbase, viewing their profiles on other sites, and exploring reviews they and others have written. By following the principles of linked data and reusing URIs, more connections can be discovered and more information provided about a subject. The conclusion states that set pivoting is made possible through linked data and following its principles allows independent sources to work as a federated network.

1. Razorbase Examples pt. 2 (Linked Data at Work) By Sherman Monroe

2. Razorbase A browser for exploring the Linked Open Data cloud using OpenLink Facets API Live demo available at http://www.razorbase.com

4. Task 1: Find a friend

10. Results set

11. Find a friend Found some things named Kingsley Idehen , surely our subject in this set

12. Find a friend Found some things named Kingsley Idehen , surely our subject in this set Now use the Alternative Ids button to pivot to his alternative identifications and profiles on the web

16. Task 2: Browse friend’s dataspaces Let’s take a look at what our friend is up to on the web

18. This looks like some kind of music related profile

19. Here’s his LinkedIn profile, hmm… let’s look more

20. Looks like a Rating/Reviews site…

21. … let’s pull this profile

23. Subject page for Kingsley Idehen’s revyu.com profile

24. List of filters for this subject

25. View all Information available about subject

27. Browse friend’s dataspace: Sparse results Only a few pieces of info for subject

28. Browse friend’s dataspace: Sparse results Only a few pieces of info for subject Let’s look at the Reverse Connections for subject

32. Browse friend’s dataspace Reverse connections are connections to other things that go the opposite direction

33. Browse friend’s dataspace Reverse connections are connections to other things that go the opposite direction An example would be Blue a color of the sky

34. Browse friend’s dataspace Reverse connections are connections to other things that go the opposite direction An example would be Blue a color of the sky We see here that the subject has reviews

38. Add information to the view

39. Add information to the view (same function)

44. Let’s scroll page right

46. New column added

49. Task 3: Lookup other mutual reviews We have pulled a review the subject made about Facebook . Let’s pivot to what others have to say about Facebook .

50. Task 3: Lookup other mutual reviews We have pulled a review the subject made about Facebook . Let’s pivot to what others have to say about Facebook . Use the Mutual Connections button

52. Let’s scroll page left

56. Mutual Connections The Mutual Connections have returned all reviewers who created this review, so it makes sense that our subject is the only result

57. Mutual Connections The Mutual Connections have returned all reviewers who created this review, so it makes sense that our subject is the only result The question is, how do we find other reviews about Facebook from here?

58. Mutual Connections The Mutual Connections have returned all reviewers who created this review, so it makes sense that our subject is the only result The question is, how do we find other reviews about Facebook from here? Let’s see if the review itself has an explicit connection to Facebook

59. Mutual Connections The Mutual Connections have returned all reviewers who created this review, so it makes sense that our subject is the only result The question is, how do we find other reviews about Facebook from here? Let’s see if the review itself has an explicit connection to Facebook From there, we can pivot to other reviews

61. Let’s return to the reviews

62. Let’s return to the reviews (same function)

67. Mutual Connections The primaryTopic information sounds useful

71. Mutual Connections We now have the topics of the reviews

72. Mutual Connections We now have the topics of the reviews We see Facebook has its very own topic

73. Mutual Connections We now have the topics of the reviews We see Facebook has its very own topic Let’s now pivot to reviews that are under the topic: Facebook

75. Let’s scroll page left

79. This column is pointing backward…

80. … to these reviews, which are filtered

81. Let’s scroll page left

83. What we want are all mutual reviews having this topic …

84. … regardless of their relationship to the subject

92. This isn’t about Facebook

93. Let’s add a filter on the primaryTopic information

96. Binds this value to the current subject (primaryTopic)

98. Now that the primaryTopic is bound to Facebook , let’s return to the reviews under this topic

100. Task 3: Explore mutual reviewers Now we have all reviews about Facebook

101. Task 3: Explore mutual reviewers Now we have all reviews about Facebook Let’s pivot to the other reviews Facebook reviewers have made

103. Let’s pull all information about these reviews

113. Explore Mutual Reviewers The query description explains the criteria and filters of your query in plain language

119. Explore Mutual Reviewers Let’s pivot to the primaryTopic of these reviews

125. Browse Mutual Reviewers I see here that revyu.com isn’t reusing URIs from other dataspaces to represent topics

127. Should be a more authoritative source, such as IMDB or DBPedia

128. Browse Mutual Reviewers Instead, revyu.com is minting it’s own topic URIs

129. Browse Mutual Reviewers Instead, revyu.com is minting it’s own topic URIs Shame on revyu.com

130. Browse Mutual Reviewers Instead, revyu.com is minting it’s own topic URIs Shame on revyu.com Watch what happens when you don’t reuse URIs used by others…

134. … users get sparse information about your subjects

136. Total information available: only 9

137. Browse Mutual Reviewers Had revyu.com used a more authorative source for it’s topic URIs (e.g. IMDB or DBPedia), then the information page could be embellished with many more connections made by others who reuse those URIs

138. Conclusion

139. Conclusion We began with a person as the subject, and ended with a list of reviews made by reviewers of Facebook , by pivoting sets and refining criteria for subjects in the query

140. Conclusion We began with a person as the subject, and ended with a list of reviews made by reviewers of Facebook , by pivoting sets and refining criteria for subjects in the query Set pivoting is possible thanks to Linked Data

141. Conclusion We began with a person as the subject, and ended with a list of reviews made by reviewers of Facebook , by pivoting sets and refining criteria for subjects in the query Set pivoting is possible thanks to Linked Data The sources in this demo are independent members of a growing federation, all following the Principles of Linked Data

142. Principles of Linked Data

143. Principles of Linked Data Use URIs as names for things

146. Principles of Linked Data Use URIs as names for things

147. Principles of Linked Data Use URIs as names for things Use HTTP URIs

150. Principles of Linked Data Use URIs as names for things Use HTTP URIs

151. Principles of Linked Data Use URIs as names for things Use HTTP URIs Provide useful information at the URI address

156. Principles of Linked Data Use URIs as names for things Use HTTP URIs Provide useful information at the URI address

157. Principles of Linked Data Use URIs as names for things Use HTTP URIs Provide useful information at the URI address Reuse and make links to URIs used by others

165. Now go grow the data web!