see http://ldl2012.lod2.eu

1. Linked Data in Linguistics
Representing and Connecting Language Data and Language Metadata
Sebastian Hellmann, Christian Chiarcos, Sebastian Nordhoff
34th Annual Meeting of the German Linguistic Society (DGfS), AG 2
Frankfurt/M., Germany, March 7th – 9th, 2012
If not otherwise noted,
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2. Overview
Technological Background (SH)
Linked Open Data and Collaborative Research (SH)
Linked Data for Linguistics (CC)
Building a Linguistic Linked Open Data Cloud
Prospects of Linked Data in Linguistics (CC)
Annotated Corpora (CC)
Lexical-Semantic Resource (SH)
Linguistic Databases (SN)
What to Expect from LDL-2012
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3. From Excel to RDF and Linked Data
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4. From Excel to RDF and Linked Data
A data collection about sailing ships:
Source http://en.wikipedia.org/wiki/File:Bounty_modified_photo.jpg
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5. From Excel to RDF and Linked Data
Add the Gorch Fock
Source http://en.wikipedia.org/wiki/File:Gorch_Fock_unter_Segeln_Kieler_Foerde_2006.jpg
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6. From Excel to RDF and Linked Data
Add the auxiliary propulsion of the Gorch Fock
The field is now irregular
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7. From Excel to RDF and Linked Data
A first empty field is introduced
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8. From Excel to RDF and Linked Data
Entity Attribute Value, data represented in triples
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9. From Excel to RDF and Linked Data
XML does also not produce sparsity or anomalies, but
what about:
1. Automatically infer rows (reduces size)
2. Check consistency (not validity)
3. Merge two tables (not only syntactically, but
semantically)
4. Enrich with external data (also retrieve updates)
5. Query
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10. From Excel to RDF and Linked Data
XML does also not produce sparsity or anomalies, but
what about:
1. Automatically infer rows (reduces size)
2. Check consistency (not validity)
3. Merge two tables (not only syntactically, but
semantically)
4. Enrich with external data (also retrieve updates)
5. Query
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11. From Excel to RDF and Linked Data
Description Logic (DL) is a family of formal knowledge
representation languages
fragments of first order logic
usually decidable inference problems
Well researched complexity
Basis for the Web Ontology Language (OWL)
Reasoner implementations available
Franz Baader, Ian Horrocks, and Ulrike Sattler Chapter 3 Description Logics. In Frank van Harmelen,
Vladimir Lifschitz, and Bruce Porter, editors, Handbook of Knowledge Representation. Elsevier, 2007.
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12. From Excel to RDF and Linked Data
Description Logic inference
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13. From Excel to RDF and Linked Data
Description Logic constraints
Possible to detect inconsistencies, i.e. Gorch Fock must
not be a Sailingship
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14. From Excel to RDF and Linked Data
XML does also not produce sparsity or anomalies, but
what about:
1. Automatically infer rows (reduces size)
2. Check consistency (not validity)
3. Merge two tables (not only syntactically, but
semantically)
4. Enrich with external data (also retrieve updates)
5. Query
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15. Uniform Resource Identifiers (URIs)
Agree on a common vocabulary and names for entities
On the schema level, coherence of properties and types
is required for data integration
URIs allow for globally unique identifiers:
“Gorch Fock”
vs.
http://en.wikipedia.org/wiki/Gorch_Fock_(1958)
vs.
http://dbpedia.org/resource/Gorch_Fock_(1958)
dbpedia:Gorch_Fock_(1958)
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16. From Excel to RDF and Linked Data
Last table before we get more technical
4 Types
of Object
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17. From Excel to RDF and Linked Data
owl:sameAs dbpedia:Gorch_Fock owl:sameAs
my:Gorch_ _(1958)
Fock
Other
datasets
my:owner
my:German
_Navy dbpedia:German_N
owl:sameAs avy
dbprop:shipLength
More data
“81.2”^^xsd:double
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18. RDF and OWL - recap
RDF – Resource Description Framework
Entity Attribute Value + URIs
Triples
Shared Vocabularies
Graphs
OWL – Web Ontology Language
Based on Description Logic and extends RDF
OWL-DL Reasoning
Consistency checks
Both are W3C standards
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19. Syntax training
Presenters will probably show you some code during the
next days
On the next slide you will see some syntax examples
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20. Serialization: Turtle and XML
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21. Serialization: Turtle and XML
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22. SPARQL
Ability to merge data and query it using the W3C
standard SPARQL (SPARQL Protocol and Query
Language)
SPARQL is the SQL of the Semantic Web
SELECT ?ship WHERE {
?ship rdf:type my:SailingShip .
?ship my:propulsion ?engine .
?engine my:fuelType my:Diesel .
?ship dbprop:shipLength ?length .
Filter (xsd:double (?length) >= 80.0 )
}
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23. Linked Data
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24. Linked Open Data cloud
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25. Linked Open Data cloud
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26. Linked Open Data cloud
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27. Linked Open Data cloud
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28. Linked Open Data cloud
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29. Linked Open Data cloud
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30. Linked Open Data cloud
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31. Linked Open Data cloud
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32. Linked Open Data cloud
Image of a table with some data
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Source http://lod-cloud.net

33. 4 Rules of Linked Data
Use URIs as names for things
Use HTTP URIs so that people can look up those
names.
When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL)
Include links to other URIs. so that they can discover
more things.
http://www.w3.org/DesignIssues/LinkedData.html
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34. Linked Data - Content Negotiation
Different views for different data consumers: Browser
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35. Linked Data - Content Negotiation
Different views for different data consumers:
Applications
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36. Linked Data
A dataset is a set of RDF triples that is published,
maintained or aggregated by a single provider.
An RDF link is an RDF triple whose subject and object
are described in different datasets
A linkset is a collection of such RDF links between two
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37. March 7th, 2012 Linked Data in Linguistics 2012 37

38. Why going for the fifth star?
Central Contractor
Registration (CCR)
Geonames
Source: http://webofdata.wordpress.com/2011/05/22/why-we-link/
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39. Open Licence allow republishing and reuse
Motivation for collaboration:
High potential that invested efforts can be reused, i.e.
data, links, vocabularies, schemas
(Effortful) feedback: Users complement data, extend
vocabularies and contribute changes. VoCamps for
achieving coherence.
Source: Chiarcos, Hellmann, Nordhoff, Towards a Linguistic Linked Open Data cloud: The
Open Linguistics Working Group, Traitement Automatique des Langues, to appear
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40. Example DBpedia
Data is extracted from Wikipedia
Wikipedia just publishes the
unstructured data
Small DBpedia team creates RDF
Community of stakeholders clean
the data and create links
Estimate:
10-20% to consolidate
community effort
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41. Scalability
Golden Hammer Anti-Pattern
Adequacy
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42. Linked Data for Linguistics
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43. Linked Data for Linguistics
Representation and modelling
Structural interoperability
Integrating distributed resources
Conceptual interoperability
Dynamic Import
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44. Representation and modelling
Structural interoperability
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45. Representation and modelling
Different linguistic subcommunities have developed
representation standards, e.g.,
LMF: Lexical Markup Framework (Francopoulo et al. 2009)
lexical-semantic resources
GrAF: Graph Annotation Framework (Ide and Suderman 2007)
for annotated corpora
based on labelled directed acyclic graphs (feature structures)
RDF data model: labelled directed (multi-)graphs
Uniform formalism for different resource types
Sublanguages (e.g., RDFS, OWL) allow to define domain-
specific vocabularies
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46. Structural interoperability
With different language resources represented in RDF,
we can combine both sources of information freely
cross-resource queries with RDF query languages (e.g.,
SPARQL)
Given a corpus with WordNet sense annotations (e.g., the
Manually Annotated Sub-Corpus MASC) (Ide et al. 2010)
“Retrieve all sentences that describe locations”
i.e., sentences containing a token annotated with a
WordNet sense that is a hyponym of “location”
Difficult to realize with GrAF or LMF
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47. Integrating distributed resources
SPARQL supports nested subqueries to run on different
repositories
No physical integration of resources in a single data
base required
Easy to link to centralized repositories of reference
terminology, etc.
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48. Conceptual interoperability
Resources should specify which vocabulary (e.g., for
annotation) they use and how it is defined
By reference to community-maintained terminology
repositories, e.g.,
GOLD (Farrar and Langendoen 2010)
ISOcat (Windhouwer and Wright @ LDL-2012)
Can be used, e.g., for disambiguation
If a lexeme in a lexicon has a certain morphosyntactic
categorization, we can retrieve all sentences from a
corpus with corresponding annotations
e.g., land as a noun, but not as a verb
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49. Dynamic import
Linking resources implemented with URIs, which can be
resolved on-the-fly to update and enrich data sets
For a token in a corpus, additional information can be
aggregated from different repositories by resolving
links (retrieving senses from a lexical-semantic
repository or concepts from a terminology
repository)
If the information in the target resource was updated
since the original annotation was performed, then the
updates are available at query time
Inconsistencies can be avoided through versioning
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50. Ecosystem, infrastructure and community
RDF and related standards are maintained by an active
and relatively large community
Different fields of application
Libraries, GeoData, BioMed, ...
Established W3C standard and technological
infrastructure
Linguistically relevant resources already provided
lexical-semantic resources (e.g., WordNet)
RDF facilitates distributed development, re-using data,
and, indirectly, interdisciplinary cooperation
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51. Building a Linguistic Linked Open Data cloud
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52. Building a Linguistic Linked Open Data cloud
In LOD cloud
Lexical Semantic
resources
Linguistic meta data
Further relevant types
for linguistic research:
Annotated corpora
Input and output of NLP
tools
Linguistic data bases
Repositories of linguistic
terminology
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53. Building a Linguistic Linked Open Data cloud
Each single provider has different incentives to use
Linked Data and/or RDF
Concepts of RDF and Linked Data have been brought
up to solve open problems in different subcommunities
of linguistics and neighboring fields
As an illustration, we briefly introduce three examples
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54. Building a Linguistic Linked Open Data cloud
Annotated corpora
Underlying problem: structural and conceptual
interoperability
Natural Language Processing for the semantic web
Underlying problem: NLP output represented in
idiosyncratic formalisms, results to be represented in
RDF
Typological data bases
Underlying problem: globally unique identifiers (not just for
“languages”, but for dialects, language families, etc.)
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55. Annotated corpora
Linked Data and Corpus Interoperability
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56. Linked Data and Corpus Interoperability
Linked Data can be used to address interoperability
issues of annotated corpora
Corpus: collection of texts developed to analyze
language and to develop tools for this purpose
=> Annotated corpora
Different types of annotations, different communities
involved, different languages
=> Interoperability challenge
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57. Linked Data and Corpus Interoperability
Linked Data can be used to address interoperability
issues of annotated corpora
Corpus: collection of texts developed to analyze
language and to develop tools for this purpose
=> Annotation
Structural interoperability Interoperable representation form
Different types of annotations, different communities
involved, different languages
Conceptual interoperability
Reference definitions for linguistic categories and features
=> Interoperability challenge
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58. Structural Interoperability
Analyses produced by different researchers / NLP tools
use different representation formalisms
word annotations
(‘tokens‘)
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59. Structural Interoperability
Analyses produced by different researchers / NLP tools
use different representation formalisms
word annotations
(‘tokens‘)
span annotations
(‘markables‘)
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60. Structural Interoperability
Analyses produced by different researchers / NLP tools
use different representation formalisms
word annotations
(‘tokens‘)
span annotations
(‘markables‘)
tree-like
annotations
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61. Structural Interoperability
Analyses produced by different researchers / NLP tools
use different representation formalisms
relational
annotations
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62. Structural Interoperability
Analyses produced by different researchers / NLP tools
use different representation formalisms
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63. Structural Interoperability
State-of-the art approaches
Graph-based data model
Represent data in standoff XML
(Ide and Suderman 2007, Chiarcos et al. 2008, Eckart et al. @ LDL)
Presentation of Nancy Ide @ LDL 2012
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64. XML standoff
MASC corpus, GrAF format
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65. Working with XML standoff
How to store, retrieve and query XML standoff data
efficiently ?
Direct use with XML data bases inefficient (Eckart 2008)
Inline XML (e.g., Dipper et al. 2007)
Relational DB formats (e.g., Eckart et al. @ LDL)
RDF as another possibility (e.g., Chiarcos 2012)
Databases are optimized for graph querying
Extensive (open source) infrastructure available
Conceptual interoperability
Integration with Linked Data resources
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66. Corpus Interoperability with RDF
Structural Interoperability
e.g. POWLA - http://purl.org/powla
lossless transformation to RDF from standoff XML
Linking to lexical-semantic resources (WordNet)
Conceptual Interoperability
Cross-Linking to terminology repositories (OLiA,
GOLD, ISOcat)
Entity-Linking to metadata (Geodata, LOD cloud)
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67. Natural Language Processing Interchange Format
NIF
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68. NLP Interchange Format (NIF)
NIF is an RDF/OWL-based format
Achieve interoperability for:
Output of NLP tools
Linguistic data in RDF
Text documents
Web of Data (LOD cloud)
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70. A Transparent Formalization of Text for
Machines
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71. A Transparent Formalization of Text for
Machines
Intransparent for machines
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72. A Transparent Formalization of Text for
Machines
Universe of discourse is defined as the words
over the alphabet of Unicode characters
URI
http://example.org/sample “The city Berlin is the capital
#offset_0_42 of Germany.”
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73. NLP Interchange Format
Specification for NIF 1.0 (http://nlp2rdf.org/nif-1-0/)
different implementations (alpha/beta) are available as
Open Source (UIMA, Gate Annie, Stanford Parser,
DBpedia Spotlight)
Mailing list available at http://nlp2rdf.org
Demo: http://nlp2rdf.lod2.eu/demo.php
Poster during the poster session
Thursday 13:00-14:30
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74. Typological databases
Glottolog/Langdoc
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75. Glottolog/Langdoc
Two subprojects
Glottolog provides identifiers and additional
information for 100k languoids (languages, dialects,
families)
main competitor projects:
ISO 639-3/Ethnologue
Multitree
Langdoc provides identifiers and additional
information for 180k references
main competitor project: OLAC
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76. Problems to address
existing identifiers are not granular enough (ISO
636-3: 7k)
existing identifiers have unclear reference (multitree
altc refers to both Micro-Altaic and Macro-Altaic)
existing identifiers have no verifiable empirical basis
Solutions
21k identifiers for main tree
total of 104k identifiers for all nodes of multitree trees
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77. RDF
gl o t t o l o g : 1 2 345 gl : s u b l an g u o i d gl o t t o l o g : 41 2 02 .
gl o t t o l o g : 1 2 345 gl : s u p e r l an g u o i d gl o t t o l o g : 9421 1 .
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78. Langdoc
180k references to literature treating (mostly) lesser-
known languages
annotated for language, document type, macro-area
limited full text indexing
“give me any grammar or grammar sketch from an Afro-
Asiatic language spoken in Eurasia where the word
'dual' occurs in the text”
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79. RDF
gl o t t o l o g : 1 2 345 gl : i mme d i at e l yd e s c r i b e d i n l an g d o c : 2 345 6 .
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80. Position of G/L in the LLOD cloud
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81. Availability
XHTML: http://glottolog.livingsources.org
RDF: http://glottolog.livingsources.org/sparql
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82. Outlook
Outlook
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83. From OWLG to DGfS
The Open Knowledge Foundation Working Group for
Open Data in Linguistics (OKFN-OWLG) was founded
in late 2010
We first established a series of meetings and a mailing
list
Build the structure, create momentum
Two workshops: OKCon 2011 in Berlin, this workshop
This afternoon: Christian Kreutz presents the OKFN
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84. Building the Linguistic Linked Data Cloud
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85. This workshop
Exploratory workshop
Chart domains as to the amount and kind of data which
can be integrated into the LLOD-cloud
increase coverage
more domains
increase density
more links between resources
increase discussion between independent
subcommunities
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86. This workshop
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87. Spread the word
http://linguistics.okfn.org/
open-linguistics@lists.okfn.org
poster at DGfS-CL session on Thursday
start this workshop: first talk:
Declerck et al.
“Towards Linked Language Data (LLD) for Digital
Humanities”
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88. We would like to thank
MPI
Springer
LOD2
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