Tutorial given at the 38th Internationalization & Unicode Conference.

1. Text
What you can make out of Linked Data
Marco Fossati <fossati@spaziodati.eu>
Steven R. Loomis <srloomis@us.ibm.com>
1

2. Let's meet the presenters
first!
2

3. Marco Fossati
Natural Language Processing
Advocate
Recommender Systems
Aficionado
Open Data
Apologist
3

4. Steven R. Loomis
IBM
Chair, Unicode ULI-TC
!
Projects:
ICU, CLDR, ULI

5. Outline
1. Linked Open Data 101
2. DBpedia
3. The ULI use case
5

6. Warning!
Highly interactive tutorial
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7. Let's get started!
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8. Text
Linked Open Data 101
The Big Picture
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9. What is data?
Data is how we express facts in a reusable form
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10. Why data? The ingredients
for...
...Information
Knowledge
Wisdom
10

11. OK it's data, what else?
Big Billions of facts “Santa
Clara is a city”
Linked Richly structured
Open Open licenses
11

12. Facts, not words
A fact is...
An assertion about the world
Subject + predicate + object
A triple
Human
mind
Natural language
!
Machine
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13. Human
mind
Perceiving
relationships
between entities
13

14. Natural language
"Elvis Presley sings Jailhouse Rock"
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15. Machine
The triple
Elvis
Presley
Jailhouse
Rock
!
sings
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16. The graph
Rich structure made of
triples
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17. From the web of documents...
Text
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18. ...to the web of entities
Text
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19. The web of entities
An entity can be...
Identified
Described through relationships
Understood both by humans and machines
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20. Towards a WWW of entities
Identify via HTTP URIs
http://dbpedia.org/resource/Elvis_Presley
Describe via RDF statements
:Elvis_presley :sings :Jailhouse_Rock
Understand via
HTML for humans
RDF for machines
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21. Hands-on Time!
https://pad.okfn.org/p/DBpediaULI
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22. Next in line…
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23. Text
DBpedia
Extracting Knowledge from Wikipedia
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24. DBpedia is…
A. …a data extraction framework
from Wikipedia semi-structured data
B. …an open-source community effort
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25. Why?
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26. Wikipedia can’t answer
simple questions
“What do Santa Clara and San Francisco
have in common?”
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27. Wikipedia can’t answer
complex questions
“Which are the black and white movies
produced in Italy that have soundtracks which
were composed by musicians who were born in
a city of the Trentino-Alto-Adige region with less
than 40,000 inhabitants?”
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28. The story so far
Project started in 2007
From good ol’ PHP to Java + Scala
Steadily growing community
Internationalization Committee
Freely available on GitHub
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29. Data in Wikipedia
Title
Short abstract
Long abstract
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30. Structure in
Wikipedia
Infobox
Images
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31. Structure in Wikipedia
Links
Categories
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32. Structure in
Wikipedia
Interlanguage Links
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33. Much more at
http://dbpedia.org/Datasets
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34. DBpedia Extraction
Framework (DEF)
Wikipedia
dump Extractors RDF graph
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35. Extractors
Article Features
Abstract, redirects, categories, geo-coordinates,
interlanguage links, etc.
Infobox
Raw
Mapping-based
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36. Raw Infobox Extractor
:Elvis_Presley
:born “Elvis Aaron Presley…”
:died “August 16, 1977…”
:restingPlace “Graceland…”
:education “L.C. Humes…”
:occupation “Singer…”
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37. The Big Issues
Data is heterogeneous!
Data is multilingual!
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38. 38

39. Solution
• The DBpedia ontology as a multilingual glue
• Wikipedia-to-ontology Mapping
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40. DBpedia
Ontology
Encoding the worldwide
encyclopedic
knowledge
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41. Mapping-based Extractor
Combines what belongs together
Separates what is different
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42. DIEF -Mapping-Based Infobox extractor
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43. The Mappings Wiki
Anybody can contribute to
mappings.dbpedia.org
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44. Download the latest
DBpedia dump at
http://downloads.dbpedia.org/
current/
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45. English SPARQL endpoint
dbpedia.org/sparql
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46. Language chapters
DBpedia in your mother tongue
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47. Active chapters
International (English-based)
Basque, Czech, Dutch, French, German, Greek,
Indonesian, Italian, Japanese, Korean, Polish,
Portuguese, Spanish
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48. Host your own language
chapter!
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49. Applications
Get the best out of DBpedia data
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50. Knowledge
Graphs
Highly informative
summaries in your
own language
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51. Text
Question Answering
“Who is Bram Stoker?”
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52. Text
Entity Linking
Detecting Things in Text
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53. Automatic
Huge
Gazetteers
Language and Domain-specific
Resources for
Short Sentences
Classification
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54. DBpedia Stakeholders
Who is using the knowledge base?
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55. Open
Government
Linking Local Data
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56. Digital
Libraries
Enriching the Catalogue
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57. Data-driven
Journalism
Building Infographics
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58. Hands-on Time!
https://pad.okfn.org/p/DBpediaULI
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59. And now the final part!
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60. Text
The ULI use case
Putting Linked Open Data to work

61. What’s wrong with
Localization Interoperability?
Inconsistent application, implementation, and
interpretation of standards
Lack of clear requirements for localization data
interchange

62. Unicode Localization
Interoperability
Technical Committee of Unicode
Focus Areas:
1. Translation memory
2. Translation source strings / translations
3. Segmentation rules

63. ULI: Segmentation
Given:
Thanks to Dr. Jones for this effort.
UAX#11 Segmentation:
|Thanks to Dr.| Jones for this effort.|
English:
|Thanks to Dr. Jones for this effort.|

64. ULI Suppression:
Abbreviations
English
Spanish
Mr.
Sr.
Mrs.
Dto.
Dr.
Sra.
St.
Avda.
…
…
Russian
проф.
февр.
тел.
кв.
…

65. Demo: ULI Breaks
http://demo.icu-project.org/icu-bin/icusegments
DEMO

66. DBpedia applied to ULI
(University of Leipzig)
Sebastian Hellman,
Martin Brümmer,
Dimitris Kontokostas
Opportunity:
Help segmentation
by supplying
abbreviation data

67. Yes!
Evaluation shows that especially for small
texts, abbreviations can contribute to
precision and recall of segmentation

68. Success rate

69. multilingual with over 100 languages
!
structured data eases extraction
!
additional data like entity types and
categories

70. Example: Mr.
“MR” disambiguation page links to “Mr.” article.
!
Ends in full stop, so may be an abbreviation.

71. The “Mr.” SPARQL query
SELECT ?entryExample ?exampleTested ?indegreeRanking
WHERE {
<http://dbpedia.org/resource/Mr.>
rdfs:label ?entryExample ;
rdfs:comment ?exampleTested .
FILTER ( lang(?entryExample) = lang(?exampleTested) )
#subselect:
{ SELECT count(?in) as ?indegreeRanking
WHERE { ?in ?p <http://dbpedia.org/resource/Mr.> }
}
}
LIMIT 100
DEMO

72. Example DBpedia data
(English)
St.
Street
<http://en.wikipedia.org/wiki/Street>
<http://schema.org/Place>
<http://dbpedia.org/ontology/Place>
<http://dbpedia.org/ontology/PopulatedPlace>

73. Example DBpedia data
(Russian)
Проф.
Профессор (Professor)
<http://ru.wikipedia.org/wiki/Профессор>

75. 1.
Get abbreviation URIs

76. 2.
Load DBpedia data into local DB

77. 3.
SPARQL Query data and tsv output

78. !
22859 abbreviations with
78197 meanings in 99
languages

79. !
22859 abbreviations with 78197 meanings in
99 languages
!
!
Long Tail
!
!
!
Only 25 languages >100 abbrevs.
!
Only 7 languages >1000 abbrevs.
!
!

80. Long tail (total abbrevs)

81. Long tail (total abbrevs) (zoom)

82. ULI Process
DBpedia
Wikipedia
ULI
Review
Extraction
Translation
Memory Translation
Memory
Translation
Memory
Comparison
Manual review
CLDR
"Lupa.na.encyklopedii" by Julo - Own work. Licensed under Public domain via Wikimedia Commons - https://
commons.wikimedia.org/wiki/File:Lupa.na.encyklopedii.jpg#mediaviewer/File:Lupa.na.encyklopedii.jpg
CLDR abbrs.
CLDR Suppressions

83. Comparison with
Translation Memory
Entry % in TM
Corp. 0.0307%
St. 0.0023%
P.T.T.C. 0%
"Trichtermitfilter" by Gmhofmann - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via
Wikimedia Commons - https://commons.wikimedia.org/wiki/File:Trichtermitfilter.jpg#mediaviewer/
File:Trichtermitfilter.jpg

84. CLDR Input
Extract abbreviations from CLDR localized data
Days of week: Sun. Mon. Tue. Wed. Thu. …
Months: Jan. Feb. Mar. …
etc…

85. Manual Review

86. CLDR output format
<segmentations>
<segmentation type="SentenceBreak">
<!--From ULI data, http://uli.unicode.org-->
<suppressions type="standard">
<suppression>Port.</suppression>
<suppression>Alt.</suppression>
<suppression>Di.</suppression>
<suppression>Ges.</suppression>
<suppression>frz.</suppression>

87. CLDR 26 Output
http://cldr.unicode.org
“Break Suppression”
de 239
en 151
es 164
fr 82
it 45
pt 170
ru 18

88. Challenges
"Long Tail" Languages
harder to find existing TM data
harder to find linguistic rules/review
harder to find tagged corpora to benchmark
Systematic issues with using redirects/disambiguation

89. Opportunity
Scope:
Non-full stop
punctuation- "Yahoo!"
Language specific
abbreviation rules
Context (Medical,
Business, …)
Leverage
Schema/Taxonomy
( “Place” vs “Person”
etc. ) to filter
DBpedia lists
Additional LOD

90. Thank You!
Further Q&A?
!
Slides & contact info:
https://pad.okfn.org/p/DBpediaULI