The document discusses the challenges of managing scattered language resources within the localization pipeline, which affects translation quality and costs. It advocates for the use of RDF and linked data standards to integrate, leverage, and enrich language resources, thereby improving efficiency and version control in language technology. The document also highlights best practices and guidelines for implementing linked data in the localization industry and the importance of metadata management.

1. RDF and other linked data
standards — how to make use
of big localization data
!Dave Lewis!
FEISGILTT Vancouver 29th Oct 2014!

2. Problem – Data Management!
— Language assets (TM and TB) scattered across the
localisation pipeline, !
– different
versions,
– different
stages
of
quality,
– in
different
‘silos’
– challenging
to
pool
&
clean
these
resources
— Difficult to share and search language resources within
and across organisations !
– impacts
on
consistency
and
cost
&
quality
of
transla;on
— Merging and extending language resources is complex!
–
makes
leveraging
new
resources
extremely
difficult

3. Why RDF/Linked Data?!
— Creates relationships (links) between data – ie linked
data!
— Easier to integrate and leverage language resources
regardless of its format, where it is stored, who owns it !
– saves
money
— Easier to search & analyse language resources !
– saves
;me
in
finding
the
most
suitable
resources
for
your
projects
— Enriches language resources with additional meaning !
–
allows
them
to
be
beCer
used
— Easy tracking of provenance !
– helps
manage
versioning

4. — Open Data on the Web: W3C Semantic Web
standards for data published on Web !
– Fine-­‐grained
inter-­‐linking
of
data
“cells”
-­‐
URL
– Extensible
meta-­‐data
–
Resource
Descrip;on
Format
(RDF)
– Standard
Query
API
-­‐
SPARQL
— LIDER Project: !
– Stakeholder
data
needs
for
LT
and
language
resources
– Best
prac;ces
and
guidelines
to
apply
linked
data
— Existing Open data vocabularies !
– Lexical-­‐conceptual
data
–
LEMON
vocabulary
– Encyclopedic
-­‐
DBPedia
– Resource
meta-­‐data,
licensing,
annota;on,
provenance
etc
Linked Data for Language Technology!

5. Linguis;c
Linked
Data:
Lexicons
Red
Phone;c
form
Form
singular
[RED]
Form
plural
[REDES]
Phone;c
form
number
number
Red
Sense
wriCen
form
“red”
Sense
wriCen
form
“malla”
equivalent
Red
image
Red
Sense
Sense
transla;on
es
-­‐
en
wriCen
form
“red”
“network”
wriCen
form
Red
wriCen
form
Form
gender
femenine
“red”

6. Use Case: BabelNet.org!

7. Data Challenges in Using LT!
— Language Technology is statistical!
— Quality is limited by distance between training data and
job at hand!
— Training Data is the Key Asset for LT!
– E.g.
for
L10n
its
Transla;on
Memories
and
Term
Bases
— Challenges for Managing Training Data!
– Discover
– Select
– Curate
– Share/Pool/Sell
– Understanding
Quality
– Measure
Impact
on
Produc;vity

8. Language
Workers
Language
Technology
Language
Resources
Active Curation: Managing LT/LR Lifecycle !
ACTIVE
CURATION

9. Use Case: FALCON Project!
Tool
Chain
• Website
transla;on
• Transla;on
Management
• Terminology
Management
Language
Technology
• Machine
Transla;on
• Term
Iden;fica;on
Linked
Data
• Parallel
Text
• Terms:
Lexical-­‐
conceptual
XLIFF
+ITS2.0
— Building the
Localization Web =
Decentralised
Annotated Global
Translation Memory
and Term Base !
— Terms and translations
become linkable
resources!
— Meta-data from L10n
tool chain adds value!
— Use in training Machine
Translation and Text
Analytics!

10. FALCON Demo: Locworld Expo!
Web
Site
Transla;on
Transla;on
Management
System
Terminology
Management
System
Machine
Transla;on
Federated
L3Data
Plagorm
Transla;on
Management
Text
Analysis
Localiza;on
Tool
chain
Language
Technology
Language
Resources
Public
Resources
DCU
TCD

11. Words as Resources on the Web!
Barak
Obama
is
the
44th
president
of
the
United
States
of
America.
He
was
first
elected
in
2008.
Barak
Obama
si
el
44
º
presidente
de
los
Estados
Unidos
de
América.
Ha
fue
electo
primera
vez
en
2008.
hCp://
www.ex.org/obama_en.html
hCp://
www.ex.org/obama_es.html
The
Web
of
Content
The
LocalizaDon
Web
hCp://data.ex.org/String_0001
hCp://
data.ex.org/String_0002
Derived
From
Derived
From
Text:
“Barak
Obama
is
the
44th
president
of
the
United
States
of
America.”
Lang:en
Text:“Barak
Obama
es
el
44
º
presidente
de
los
Estados
Unidos
de
América.”
Lang:es
TranslatedBy:Google
Translate
Translated
From
TranslaDon
Data
Term:
“United
States
of
America.”
Lang:en
Term:“Estados
Unidos
de
América.”
Lang:es
Transla;on
Of
hCp://
babelnet.org/345621
hCp://
babelnet.org/57835
Terminology
Data
Topic:
Barack
Obama
Lang:
en
BirthDate:
1961-­‐08-­‐04
Spouse:
Michelle
Obama
Residence:
White
House
hCp://
Dbpedia.org/Page/
Barak_Obama
Encyclopaedic
Data

12. L10n Use Case: Closing the Loop!
— Active Curation: Systematic harvesting of
LT-ready TM and TB from localization tool chain!
— Data and Tools for Optimise process flow:!
– Priori;ze
segments
for
postedi;ng
and
input
to
incremental
MT
retraining
– Target
postedits
to
extract
target
terms
and
new
morphologies
— Postediting Instrumentation:!
– Postedit
;me
and
resource
use
(terms,
concordance)
vs.
automa;on
of
MT
metrics
– iOmegaT:
instrumented
open
source
CAT
tool
— LREC, AMTA, EDF, MLW, LocFoc, Multilingual, EdMedia,
FEISGILTT!

13. Research and Innovation Roadmap!
— https://www.w3.org/community/ld4lt/wiki/
Linguistic_Linked_Data_for_Content_Analytics:_a_Roadmap!
Global
Customer
Engagement
Use
Cases
Public
Sector
and
Civil
Society
Use
Cases
LinguisDc
Linked
Data
Life
Cycle
and
Value
Network
Requirements

14. Best Practices for Multilingual Linked
Open Data!
— Linguistic Vocabularies. !
— Resource-specific vocabularies!
— Best Practices for Multilingual Linked
Data !
–
Prac;ces
for
Naming.
– Prac;ces
for
Dereferencing
– Prac;ces
for
Textual
Informa;on
– Prac;ces
for
linking.
– Iden;fica;on
of
languages
.
– DataID
– OWL
Metamodel
for
Language
Resources
– License
Ontology
— Guidelines for Converting WordNets
to Linked Data !
— Guidelines for Linguistic Linked Data
Generation: Multilingual Knowledge
Bases.!
— Guidelines for Linguistic Linked
Data Generation: Bilingual
Dictionaries !
— Guidelines for Converting TBX into
Linked Data!
— Guidelines for NIF-based NLP
Services !
— Comparison of Repositories!

15. Dublin Workshop Session!
— There is a need for a common API to text analysis services, live
update of linked data source, user feedback mechanisms, or
annotation relevance indicators.!
— "Too much information is no information": linked data information can
help the translator only if it does not lead to an information overflow.!
— A stand-off annotation mechanism is needed to deal with annotation
overlap. NIF could be a solution.!
— For the localization industry, licensing metadata is of key importance.
Only with such metadata one can also work with internal = closed
linked data.!
— Terminology and linked data is a hot topic discussed also in
the LD4LT group. Currently there is no standard mapping of the TBX
format to RDF.!
— Bitext (= aligned text of a source and one or several translations)
could be exposed as as linked data, as an alternative to TMX.!

16. More Information!
— Contact: dave.lewis@cs.tcd.ie!
– hCp://www.falcon-­‐project.eu
— Lider: best practices and roadmap!
– hCp://www.lider-­‐project.eu/
— See also: !
– Linked
Data
for
Language
Technology
(LD4LT)
W3C
Community
Group
• hCp://www.w3.org/community/ld4lt/
– Best
Prac;ce
in
Mul;lingual
Linked
Open
Data
• hCp://www.w3.org/community/bpmlod/
– OntoLex
Community
Group
• hCp://www.w3.org/community/ontolex/