This workshop will introduce participants to Linked Data, a key semantic web technology, and its uses in the digital humanities. Through examples of Linked Data websites and applications, we will explore how Linked Data is being used by individual digital humanities scholars, by organisations such as the BBC and the Central Statistics Office, and by cultural heritage institutions worldwide. We will make comparisons to other approaches to structuring data (including markup and metadata approaches such as TEI and XML) and discuss best practices for creating and reusing Linked Data (such as the importance of identifiers and standard vocabularies). Participants will also be introduced to tools for creating and exploring Linked Data. The workshop will also include a hands-on exercise in creating Linked Data. Linked Data in the Digital Humanities was a Skills Workshop http://dri.ie/skills-workshops part of Realising the Opportunities of Digital Humanities http://dri.ie/realising-opportunities-digital-humanities Presenters: Jodi Schneider and Michael Hausenblas with support from Stefan Decker, Nuno Lopes, and Bahareh Heravi all of the Digital Enterprise Research Institute, National University of Ireland Galway

1. Digital Enterprise Research Institute www.deri.ie
Linked Data in the Digital Humanities
Jodi Schneider & Michael Hausenblas
with Stefan Decker & Nuno Lopes
Realising the Opportunities of Digital Humanities Thursday 25th October 2012
National University of Ireland, Maynooth
Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
Enabling Networked Knowledge
1

2. (2) 2

3.  What is Linked Data? Why use it? What are some
examples?
 How do Linked Data applications differ from
conventional ones?
 How is Linked Data different from other structured
data used in digital humanities? (e.g. TEI, XML)
 What are the best practices for creating Linked Data?
(3) 3

4.  Using identifiers
 to enable access
 to add structure
 to link to other stuff
(4) 4

5. (5) 5

6. Photo credit “nepatterson”, Flickr

7.  A “Web” where
 documents are available for download on the Internet
 but there would be no hyperlinks among them
(7)
Slide credit: Ivan Herman
7

10.  We need a proper infrastructure for a real Web of Data
 data is available on the Web
• accessible via standard Web technologies
 data are interlinked over the Web
 ie, data can be integrated over the Web
 We need Linked Data
(10)
Slide credit: Ivan Herman 10

11. Photo credit “kxlly”, Flickr

12.  Mass Media
 BBC
 New York Times
 Guardian
 Scholarly Publishers
 Nature
 CrossRef
 Data Publishers
 USData.gov
 Data.gov.uk
 Central Statistics Office
 Libraries
(12) 12

13. (13) 13

14.  http://www.ted.com/talks/tim_berners_lee_the_year_o
pen_data_went_worldwide.html
(14) 14

15. (15) 15

16. http://5stardata.info
(16) 16

17. (17) 17

18. core record
DBpedia
record
Europeana
record
app records app
British Library
record
data.gov.ie
record
conventional back-end Linked Data back-end

19. core record
DBpedia
record
Europeana
record
app records app
British Library
record
data.gov.ie
record
conventional back-end Linked Data back-end

20. (20) 20

21. http://county-rank.data-gov.ie

22. http://county-rank.data-gov.ie

23. http://school-explorer.data-gov.ie

24. (24) 24

25. http://srvgal85.deri.ie/ab-app/

26. user
Dydra
ab-proxy.py
(120 LOC)
DBpedia
ab-app.js Europeana
(181 LOC)
client-side DERI server-side other server

27. (27) 27

28. (28) 28

29.  Using identifiers
 to enable access
 to add structure
 to link to other stuff
(29) 29

30. Remember, everything must nest properly!
Document
Paragraph Paragraph
Sentence Sentence Sentence Sentence Sentence
We use family tree terms: parent, child, sibling, ancestor, and descendent.
Slide credit: Susan Schreibman
(30) 30

31. (31) 31

32. structural divisions within a text
title-page, chapter, scene, stanza, line, etc
typographical elements
changes in typeface, special characters, etc
other textual features
grammatical structures, location of illustrations, variant
forms, etc
Slide credit: Susan Schreibman
(32) 32

33. structural divisions within a text
title-page, chapter, scene, stanza, line, etc
typographical elements
changes in typeface, special characters, etc
other textual features
grammatical structures, location of illustrations, variant
forms, etc
Slide credit: Susan Schreibman
(33) 33

34. (34) 34

35.  Using identifiers
 to enable access
 to add structure
 to link to other stuff
(35) 35

36. http://www.youtube.com/watch?v=TJfrNo3Z-DU
(36) 36

37. (37) 37

38. (38) 38

39. dog =
Slide Credit: Karen Coyle
(39) 39

40. hund (lang=de)
dog (lang=en)
perro (lang=sp)
IDa87nn3
chien (lang=fr)
Slide Credit: Karen Coyle
(40) 40

41. Slide Credit: Karen Coyle
(41) 41

42. Slide Credit: Karen Coyle
(42) 42

43.  A Uniform Resource Identifier (URI) is a compact
sequence of characters that identifies an abstract
or physical resource. [RFC3986]
 Syntax
URI = scheme ":" hier-part [ "?" query ] [ "#" fragment ]
 Example
foo://example.com:8042/over/there?name=ferret#nose
_/ _________________/____/ _______________/
____/
| | | | |
scheme authority path query fragment
Slide Credit: Michael Hausenblas
(43) 43

44.  http://dbpedia.org/page/Maynooth
(44) 44

45.  http://dbpedia.org/page/Maynooth
(45) 45

46. (46) 46

47.  Using identifiers
 to enable access
 to add structure
 to link to other stuff
(47) 47

48. http://www.youtube.com/watch?v=TJfrNo3Z-DU#t=1m20.5s
(48) 48

49. (49) 49

50. (50) 50

51. (51) 51

52.  Title
 Creator
 Date
 Subject
 Contributor
 Coverage
 Description
 Format
 Identifier
 Publisher
 Relation
 Rights
 Source
 Type
(52) 52

53. Slide credit: Dan Brickley
(53) 53

54. Slide credit: Dan Brickley
(54) 54

55. Slide credit: Dan Brickley
(55) 55

56. Slide credit: Dan Brickley
(56) 56

57. Slide credit: Dan Brickley
(57) 57

58. Slide credit: Dan Brickley
(58) 58

59. Slide credit: Dan Brickley
(59) 59

60.  Which vocabularies might you use?
 What might you need vocabularies to represent?
(60) 60

61. (61) 61

62.  What is Linked Data? Why use it? What are some
examples?
(62) 62

63.  Using identifiers
 to enable access
 to add structure
 to link to other stuff
(63) 63

64. Photo credit “nepatterson”, Flickr

65. Photo credit “kxlly”, Flickr

66. http://5stardata.info
(66) 66

67.  What is Linked Data? Why use it? What are some
examples?
 How do Linked Data applications differ from
conventional ones?
(67) 67

68. core record
DBpedia
record
Europeana
record
app records app
British Library
record
data.gov.ie
record
conventional back-end Linked Data back-end

69.  What is Linked Data? Why use it? What are some
examples?
 How do Linked Data applications differ from
conventional ones?
 How is Linked Data different from other
structured data used in digital humanities? (e.g.
TEI, XML)
(69) 69

70. (70) 70

71.  What is Linked Data? Why use it? What are some
examples?
 How do Linked Data applications differ from
conventional ones?
 How is Linked Data different from other structured
data used in digital humanities? (e.g. TEI, XML)
 What are the best practices for creating Linked
Data?
(71) 71

72.  What is Linked Data? Why use it? What are some
examples?
 How do Linked Data applications differ from
conventional ones?
 How is Linked Data different from other structured
data used in digital humanities? (e.g. TEI, XML)
 What are the best practices for creating Linked Data?
(72) 72

73. (73) 73

74. dog =
Slide Credit: Karen Coyle
(74) 74

75.  http://dbpedia.org/page/Maynooth
(75) 75

76. (76) 76

77.  Title
 Creator
 Date
 Subject
 Contributor
 Coverage
 Description
 Format
 Identifier
 Publisher
 Relation
 Rights
 Source
 Type
(77) 77

78. (78) 78

79. 79
(79) 79

80. (80) 80

81. (81) 81

82. (82) 82

83. (83) 83

84. (84) 84

85. (85) 85

86. (86) 86

87. (87) 87

88. Digital Enterprise Research Institute www.deri.ie
Thanks to our funders!
Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
Enabling Networked Knowledge

89. (89) 89

90. (90) 90

91. (91) 91

94. (94) 94

95. (95) 95

96. (96) 96

97. (97) 97

98. (98) 98

99. AKA “An introduction to the Semantic Web (Through an Example)” by Ivan Herman
(99) 99

100.  Map the various data onto an abstract data
representation
 make the data independent of its internal representation…
 Merge the resulting representations
 Start making queries on the whole!
 queries not possible on the individual data sets
(100) 100

102. ISBN Author Title Publisher Year
0006511409X id_xyz The Glass Palace id_qpr 2000
ID Name Homepage
id_xyz Ghosh, Amitav http://www.amitavghosh.com
ID Publisher’s name City
id_qpr Harper Collins London
(102) 102

103. The Glass Palace
http://…isbn/000651409X
2000
London a:author
Harper Collins
a:name
a:homepage
Ghosh, Amitav http://www.amitavghosh.com
(103) 103

104.  Relations form a graph
 the nodes refer to the “real” data or contain some literal
 how the graph is represented in machine is immaterial for now
(104) 104

106. A B C D
1
ID Titre Traducteur Original
2
ISBN 2020286682 Le Palais des Miroirs $A12$ ISBN 0-00-6511409-X
3
4
5
6
ID Auteur
7
ISBN 0-00-6511409-X $A11$
8
9
10
Nom
11
Ghosh, Amitav
12
Besse, Christianne
(106) 106

107. http://…isbn/000651409X Le palais des miroirs
f:auteur
http://…isbn/202038668
2
f:traducteur
f:nom
f:nom
Ghosh, Amitav
Besse, Christianne
(107) 107

108. The Glass Palace
http://…isbn/000651409X
2000
London
a:author
Harper Collins
a:name http://…isbn/000651409X
a:homepage
Le palais des miroirs
Ghosh, Amitav
http://www.amitavghosh.com
f:auteur
http://…isbn/2020386682
f:traducteu
r
f:nom
f:nom
Ghosh, Amitav
Besse, Christianne
(108) 108

109. The Glass Palace
http://…isbn/000651409X
2000
London Same URI!
a:author
Harper Collins
a:name http://…isbn/000651409X
a:homepage
Le palais des miroirs
Ghosh, Amitav
http://www.amitavghosh.com
f:auteur
http://…isbn/2020386682
f:traducteu
r
f:nom
f:nom
Ghosh, Amitav
Besse, Christianne
(109) 109

110. The Glass Palace
http://…isbn/000651409X
2000
London
a:author
Harper Collins f:original
a:name f:auteur
a:homepage
Le palais des miroirs
Ghosh, Amitav
http://www.amitavghosh.com
http://…isbn/2020386682
f:traducteu
r
f:no
m
f:nom
Ghosh, Amitav
Besse, Christianne
(110) 110

111.  User of data “F” can now ask queries like:
 “give me the title of the original”
• well, … « donnes-moi le titre de l‟original »
 This information is not in the dataset “F”…
 …but can be retrieved by merging with dataset “A”!
(111) 111

112.  We “feel” that a:author and f:auteur should be the
same
 But an automatic merge doest not know that!
 Let us add some extra information to the merged
data:
 a:author same as f:auteur
 both identify a “Person”
 a term that a community may have already defined:
• a “Person” is uniquely identified by his/her name and, say,
homepage
• it can be used as a “category” for certain type of resources
(112) 112

113. The Glass Palace
http://…isbn/000651409X
2000
Le palais des miroirs
f:original
London
a:author http://…isbn/2020386682
Harper Collins f:auteur
r:type f:traducteu
r:type r
a:name
a:homepage http://…foaf/Person
f:nom
f:nom
Besse, Christianne
Ghosh, Amitav
http://www.amitavghosh.com
(113) 113

114.  User of dataset “F” can now query:
 “donnes-moi la page d‟accueil de l‟auteur de l‟original”
• well… “give me the home page of the original‟s „auteur‟”
 The information is not in datasets “F” or “A”…
 …but was made available by:
 merging datasets “A” and datasets “F”
 adding three simple extra statements as an extra “glue”
(114) 114

115.  Using, e.g., the “Person”, the dataset can be
combined with other sources
 For example, data in Wikipedia can be extracted
using dedicated tools
 e.g., the “dbpedia” project can extract the “infobox” information
from Wikipedia already…
(115) 115

116. The Glass Palace
http://…isbn/000651409X
2000
Le palais des miroirs
f:original
London
a:author http://…isbn/2020386682
Harper Collins f:auteur
r:type f:traducteu
r
a:name r:type
a:homepage http://…foaf/Person
f:no
m f:nom
r:type
Besse, Christianne
Ghosh, Amitav http://www.amitavghosh.com
foaf:name w:reference
http://dbpedia.org/../Amitav_Ghos
h
(116) 116

117. The Glass Palace
http://…isbn/000651409X
2000
Le palais des miroirs
f:original
London
a:author http://…isbn/2020386682
Harper Collins f:auteur
r:type f:traducteu
r
a:name r:type
a:homepage http://…foaf/Person
f:nom
f:nom
r:type
w:isbn
Besse, Christianne
Ghosh, Amitav http://www.amitavghosh.com
foaf:name w:reference http://dbpedia.org/../The_Glass_Palace
w:author_of
http://dbpedia.org/../Amitav_Ghos
h
w:author_of
http://dbpedia.org/../The_Hungry_Tide
w:author_of
http://dbpedia.org/../The_Calcutta_Chromosome
(117) 117

118. The Glass Palace
http://…isbn/000651409X
2000
Le palais des miroirs
f:original
London
a:author http://…isbn/2020386682
Harper Collins f:auteur
r:type f:traducteu
r
a:name r:type
a:homepage http://…foaf/Person
f:nom
f:no
r:type
m
w:isbn
Besse, Christianne
Ghosh, Amitav http://www.amitavghosh.com
foaf:name w:reference http://dbpedia.org/../The_Glass_Palace
w:author_of
http://dbpedia.org/../Amitav_Ghos
h w:born_in
w:author_of http://dbpedia.org/../Kolkata
http://dbpedia.org/../The_Hungry_Tide
w:long w:lat
w:author_o
f
http://dbpedia.org/../The_Calcutta_Chromosome
(118) 118

119.  It may look like it but, in fact, it should not be…
 What happened via automatic means is done every
day by Web users!
 The difference: a bit of extra rigour so that machines
could do this, too
(119) 119

120.  We could add extra knowledge to the merged
datasets
 e.g., a full classification of various types of library data
 geographical information
 etc.
 This is where ontologies, extra rules, etc, come in
 ontologies/rule sets can be relatively simple and small, or
huge, or anything in between…
 Even more powerful queries can be asked as a result
(120) 120

121. Manipulate
Applications Query
…
Map,
Data represented in abstract format Expose,
…
Data in various formats
(121) 121

122. (122) 122