Europeana meeting under Finland’s Presidency of the Council of the EU - Day 1, 24 october 2019

Books on a table, Aalto, Ilmari, 1928, National Digital Library (NDL), Finland, CC0
EUROPEANA MEETING
UNDER FINLAND’S PRESIDENCY
OF THE COUNCIL OF THE EU
ESPOO, FINLAND
24 October 2019

Andy Neale
Technical Director
Europeana Foundation

Europa [Material cartográﬁco] : Nach den vorzüglichsten Hülfsnitteln, Götze, Johann August Ferdinand, 1773-1819 Biblioteca Digital de Madrid Spain, Public domain
Contribution to EU GDP
by culture and creative sectors
Trade Surplus
in cultural goods
€ 8.7B 4.2%
New Agenda for Culture

Automotive + Manufacturing +
Chemical Industries
Cultural + Creative
Sector
7.8M 4.4M>
Employment

young professionals
(15-29 yrs old)
19.1%

The role of
Europeana
Europeana Party People @ Christmas party, CC BY

We support cultural heritage institutions in
their digital transformation

3.700
CHIs across Europe

EUROPEANA COLLECTIONS
58m
Cultural heritage records
2.5bln
Information items

1. Common Tech & data architecture
Europeana
Data Model +
Metis

2. Common policies & standards
Europeana
● Licensing
Framework
● Publishing
Framework

Statements for works that are
not in copyright
Statements for works where the
copyright status is unclear
Statements for works that are in
copyright

3. Websites & APIs
Europeana
Collections

Programme
Europeana Party People @ Christmas party, CC BY

Objectives
1. Stimulate reﬂection on multilingualism in digital cultural heritage at
large using Europeana as a case study;
2. Develop a deeper understanding of the multilingualism
problem/opportunity space for digital cultural heritage;
3. Consider what options can be pursued to provoke action at the local
level, furthering the multilingual capabilities;
4. Provide input and feedback for the Europeana multilingual strategy.

Sessions
1. Setting the scene
2. User interactions
3. Multilingual metadata
4. Content translation
5. Conclusions and steps for progress

Juliane
Stiller
Information Specialist
You, We & Digital
‘Multilingual Developments in
Digital Cultural Heritage Domain:
Problem Space & Solutions’

20
● 10 years researcher at
Humboldt-Universität zu
Berlin in Europeana-related
projects
● multilinguality, interaction
patterns, metadata and its
quality, research on search
and browse, retrieval,
evaluation
● since 2019 consultancy and
training in digital literacy
@stillinsky

Agenda
• Multilinguality: the problem space
• Bridging the language gap
• Translations
• Enrichments
• What is left to do?
21

22
Multilinguality
The Problem Space
Christoﬀel van Sichem: Bouw van de toren van Babel

Content
Information Access
Interactions
User Interface
Metadata and digital
CH objects
Search, Browse & Explore
Show user‘s
preferred language
Bridge the gap between
language of user input
and content
Layers of digital CH system

User Interface
24
Challenges:
• Translation of static and dynamic
pages
• Switching languages via text or
icons such as ﬂags
• Default language
• Determine the user‘s preferred
language through IP address or
browser settings
User Interface

Interactions: Search
25
Interactions

Mismatch between query and
content language
• Mona Lisa 203 results
• Monna Lisa 13 results
• La Gioconda 376 results
• La Joconde 78 results
26
Interactions
Roma, Galleria Corsini - La
Gioconda,

Interactions: Browse
● Search vs. browse
● (Metadata) text vs. object
27
Interactions

Interactions: Explore
cater for diﬀerent information needs in diﬀerent languages:
• Entities
• Colors, format
• Access & copyrights
• Inspiration
28
Interactions

Content & Metadata
29
Image Credit: both from Europeana with Titlte „Kinderbuch” from
Spielzeugmuseum der Stadt Nürnberg (CC BY-NC-SA)
Content

Metadata multilinguality
30+ 40 other languages....
Content

Bridging the language gap
Translations & Enrichments
31
Bridge by Mark Robinson (CC-BY 2.0)

To bridge the gap between language of user
input and content, one can translate
1. Queries
2. Content / Metadata
3. .....
32

1) Translating queries
33
Query
English
Spanish
French
....
comes with challenges ....
Database
Information Access

Cultural heritage queries
34
κερκυρα
poblet
bævre
humble østerskovvej
espana salamanca
academia coleccam
documentos estatutos
εσκι σεχιρ
ﬁrst war world
berlin berliner mauer or wall
alphonse mucha

Query heterogeneity & long tail
35
Europeana queries in a month in 2016
442 times: Wolfgang Amadeus
Mozart
once: full history of ging
tsholing in bhutan

Queries in cultural heritage are
● Short
● Heterogeneous
● Focus on entities: 61.96% of the queries contain NE (Stiller, Gäde &
Petras, 2010)
● Highly ambiguous in language:
○ “culture”, “administration”, “paris”, “madonna”
● Semantically ambiguous:
○ “barber” (composer or hairdresser)
36

Multilingual academic search
● informational queries from the psychology domain in 4
languages: pubpsych.eu
● Buildung domain-specific lexical resources and map them
to queries; entries look like this:
○ wohlbefinden|||en:well-being|||es:bienestar|||fr:bien-etre
○ wohlfuhlen|||en:well-being|||es:bienestar|||fr:bien-etre
○ Well-being|||es:bienestar|||de:wohlbefinden|||fr:bien-etre
● Translation does not depend on language identification
● Deals well with NE -> no match in Lexicon, no translation
More Info on the project: https://www.clubs-project.eu/en/

Query
2) Translate the content
38
Spanish
French
German
English
Content
English
French
German
Spanish
Content
Database

Metadata
heterogeneity &
sparsity
39
http://www.europeana.eu/port
al/en/record/92022/Bibliograp
hicResource_1000125938148.ht
ml
https://www.europeana.eu/portal/en/record/92022/Bibl
iographicResource_1000125938148.html

Challenges
• Missing training data for small languages
• Missing training data for (sub)domains
• Amount of language pairs is immense with 50+
languages
• Metadata is too scarce for good translation results
40

Number of enriched objects, their type and
vocabularies
GeoNames
7 Millions
GEMET, DBpedia
9.2 Millions
Semium Time
10.2 Millions
DBpedia
144,000
Time Concept
Locations
Agents
Enriched entities in Europeana

Semantically incorrect
enrichment
Polen (Dutch) Polen (Basque)

Adapt to queries
Entity graphs for
exploration
• Object
• Person
• Concept
• Period
• Location
• Event
46

Evaluate solution based on goal
○ E.g. for ML retrieval we might not need the perfect fluent
translation
○ Identify the impact of different workflows / processes on
multilinguality of system
○ Translations do not only have an impact on data but also on
retrieval and therefore on user satisfaction
47

Thank you!
http://tatecollectives.tumblr.com/tagged/1840s-GIF-Party
48
@stillinsky
hello@you-we-digital.com

References
• Petras, V., Hill, T., Stiller, J., & Gäde, M. (2017). Europeana – a Search Engine for Digitised Cultural
Heritage Material. Datenbank-Spektrum, 1–6. https://doi.org/10.1007/s13222-016-0238-1
• Hill, T. D., Charles, V., Isaac, A., & Stiller, J. (2016). “Searching for Inspiration”: User Needs and Search
Architecture in Europeana Collections. ASIS&T 2016 Annual Meeting.
• Manguinhas H (2016) Europeana Semantic Enrichment Framework. Documentation, Europeana.
https://docs.google.com/document/d/1JvjrWMTpMIH7WnuieNqcT0zpJAXUPo6x4uMBj1pEx0Y
• Stiller, J. (editor) )(2016) Best practices for multilingual access. Tech. rep.
http://pro.europeana.eu/ﬁles/Europeana_
Professional/Publications/BestPracticesForMultilingualAccess_whitepaper.pdf
• Stiller, J., Gäde, M., & Petras, V. (2013). Multilingual access to digital libraries: The Europeana use
case. Information-Wissenschaft Und Praxis, 64, 86–95.
• Olensky, M., Stiller, J., & Dröge, E. (2012). Poisonous India or the Importance of a Semantic and
Multilingual Enrichment Strategy. In 6th Research Conference, MTSR 2012, Cádiz, Spain, November
28-30, 2012. (pp. 252–263). Berlin: Springer.
• Stiller, Gäde, Petras (2010): Ambiguity of Queries and the Challenges for Query Language Detection.
49

Rickard
Domeij
Language Planner
Language Council of Sweden, Institute of
Language and Folklore
Multilingualism, technology
and language policy

Content
● The LC and the multilingual language policy of Sweden (and EU)
● Multilingually accessible services
● Language technology (LT) and language resources
● National Language Bank
● First experiences in digital humanities and cultural heritage
● Challenges for LT in cultural heritage
● Next steps

Multilingual language policy
● The LC monitors and promotes the languages of Sweden and their use
● Language policy (2005) and Language act (2009)
● Status and rights to use Swedish and other languages in Sweden
● National minority languages: Sami, Meänkieli, Finnish, Romani, Jiddish
● Swedish sign, Nordic languages, EU-languages, immigrant languages
● Public agencies have to reach out to the whole population
● Also good for business

Multilingually accessible services
● Vision: a multilingual society in which all citizens are included with
respect to diﬀerent backgrounds and languages --> digital inclusion
● Access to info and services according to language rights and needs
● Switch between languages and modes according to preferences
● Example: have a web text read aloud in your language
● Essential for people with disabilities but also useful for others = design
for all (e.g. subtitling)

LT to make it possible
● Conversions between languages and modes
● Diﬀerent modes: writing, speech, gestures …
● Multilingualism = multilinguality + multimodality
● LT modules: text-to-speech (TTS), speech-to-text (STT), machine
translation (MT) …
● Applications: recitation, dictation, translation …
● Voice translation: STT > MT > TTS

LT to make it possible II
● Problems with quality and trust, especially on unrestricted data
● User and domain adaptation, user interaction
● Ex: respeaking system for subtitling on tv
● Accessibility often means loss of quality, but other gains
● Accessible and usable

Language resources needed
● Data and tools: corpora, markup tools, lexicons, language models …
● Rule-based methods, especially for less resourced languages
● Market forces are not enough
● Stimulate the development of LT and multilingually accessible services
by national means (ex: respeaking system for Swedish tv)
● National Language Bank (NLB) to make resources available for R&D
An NLB promotes the development of technology, which beneﬁts the languages in
Sweden and improves access to information for everyone.
Digital agenda for Sweden (2011)

National research infrastructure (2017-
00626) funded by the Swedish Research
Council by 1,5 mil./year until 2025.
Two main types of data:
Multilingual texts and terms from PAs
Multimodal cultural heritage collections

First experiences in cultural heritage
● Available voice recognition and MT doesn’t work!
● Instead try other methods:
○ ”sound browsing” to explore speech recordings acoustically
○ respeaking for transcribing speech
○ transcription of handwritten dialect text in Transcribus
○ time-alignment of existing transcripts to sound in ELAN
○ linking from text to speech data in the archives (see next page)
● Usage centered, participative design in multidisciplinary teams
● Tilltal project (SAF16-0917:1)

First experiences in cultural heritage
● State-of-the-art voice recognition and MT doesn’t work!
● Instead try other methods:
○ ”sound browsing” to explore speech recordings acoustically
○ respeaking for transcribing speech
○ transcription of handwritten dialect text in Transcribus
○ time-alignment of existing transcripts to sound in ELAN
○ linking from text to speech data in the archives
● Usage centered, participative design in multidisciplinary teams
● Tilltal project (SAF16-0917:1)

Challenges for LT in cultural heritage
● Interface or content (= multilingual in a broad sense)
● Far beyond modern standard language use
● Great variation makes domain adaptation hard
● Variation in place (dialects and languages), time (old Swedish) and
situation (informal-formal)
● Modal variation in collections: (handwritten) text, speech, pictures
● Hard to handle as researchers want to explore a collection as a whole

Next steps
● Linked data to describe the collection conceptually and relationally
● Multilingual search methods for handling language variation in place,
time and situation
● Domain adopted speech-to-text conversion to transcribe recordings
● Crowdsourcing for correcting
● Shared resources for the languages, dialects, domains etc
● Long time funding for the National Language Bank
● Collaborative projects involving LTists, researchers and data holders

Andrejs Vasiļjevs
Executive Chairman
Tilde
Project Manager
Culture information systems centre Riga
Jānis Ziediņš
Learnings from the automatic
translation projects and how to
apply them for the culture and
heritage sector

Culture information systems centre
65
Our mission is to assist cultural heritage institutions -
ARCHIVES LIBRARIES MUSEUMS
maintain and make available cultural heritage for future generations
through the latest information technology solutions.

Benefit for eGovernment
6
8
State Gov.lv platform
Platform for the
provision and
management of
e-Services
Single Public Administration
Data Area
Municipality IS
Other IS
State information systems
MT platform
OpenData

Digitization of the Cultural Heritage Content
The National Library of Latvia is implementing a European Regional Development
Fund (ERDF) and nationally co-funded project in the field of Latvia's digital cultural
heritage, together with project partners – the National Archives of Latvia, the State
Inspection for Heritage Protection of Latvia, and the Cultural Information System
Centre.
The project will further develop the Digital Object Management and Conservation
System, develop the Copyright Management and Content Licensing System, publish
several Open Datasets, including Related Open Datasets, and develop the Stage of
an Integrated Centralized Open System Information Platform.
7
7

Translation test
A photomontage postcard with five
views of Riga. The central city
panorama with the new Pontoon
Bridge opened in 1896 and the Mazā
Guild building in the right corner.
Below these images, the city theatre,
Vērmanes Garden and the bridge
across the canal by Bastejkalns.
A postcard is assembled from five views
of Riga - downtown panorama with the
new Pontonbridge discovered in 1896,
the Little Guild House in the right
corner, under these images - City
Theatre, Verman Gardens, a bridge
over the canal near BastejHill.
Manual translation Hugo.lv translation
VRVM 176655 http://www.nmkk.lv/Items/ItemViewForm.aspx?i
d=167748
8
0

AI for breaking
language barriers

Enablers of AI
ML Algorithms Computing PowerBig Data

84
Based on Tilde Neural MT
technologies that have won the
1st place at the
WMT2017-2019, a global
competition between the
World’s top language
technology providers
Best
WMT
2017
Best
WMT
2018
Best
WMT
2019

• Generic MT systems were
trained on
52 million parallel sentences
• Cultural domain MT systems
were customized with
additional
826 000 parallel sentences
5 million monolingual
sentences
Books
Public sector
data
▪ Fiction
▪ Scientific literature
▪ Technical literature (manuals, instructions)
▪ News from popular media (also multilingual
media)
▪ Company press releases
▪ Multilingual web site content
▪ Laws, regulations, directives, etc.
▪ Documents of internal and external use
▪ Press releases
▪ Public sector web site data
News and web
content
Proprietary
translation
memories
▪ Professional and amateur translator produced
data
▪ Translation memories of translation and
localisation service providing companies
▪ Translation memories of international
organisations
Datafor MT System
Development

Comparison to Google –
Automatic Evaluation

Comparison to Google –
Human Evaluation

Usability, productivity and
integration
Translation add-on
for browsers
Translation API
Plug-ins for
CAT tools
Translation widget

Hugo.lv – AI powered language technology portal

91
1.1
million terms
22
subject ﬁelds
164 216
terms in culture
domain

92
EU Council Presidency Translator
2017-2020

93
EU Presidency bildīte
EU COUNCIL PRESIDENCY
TRANSLATOR

94
EU PRESIDENCY TRANSLATOR
AI-powered Neural MTCEF eTranslation
MT systems for the 24 ofﬁcial
EU languages enabling
translation of full documents,
preserving text formatting
AI-powered custom Neural
MT providing superior-quality
translation adapted for the
Presidency requirements

95
Web Site – Text Translation

96
Formatting-Rich Document Translation

98
BENEFITS FOR ESTONIA, BULGARIA, AUSTRIA
• Enables Presidency staff to quickly translate documents
• Empowers visiting journalists and delegates to access info in
the local language, e.g., press releases, local news sites
• Supports staff translators in their work by boosting
translation productivity up to 35%
• Lowers costs of translation for documents by utilizing
post-edited machine translation
• Allows public sector organizations to translate content and
websites into multiple languages

99
From September, 2017 to October, 2019 the EU Council
Presidency Translator has processed:
32 159 082
million
words
2.83
million
sentences
1.09
million
translation
requests
~207 books (there are 155
thousand words on average in one
Harry Potter book)
STATISTICS

101
Conclusions
• New generation of Neural MT strongly improves quality and applicability of
machine translation, especially for morphology rich languages
• Domain specific data is crucial for making MT suitable for cultural and other
domains
• Depending on the application, translation needs can be served by selecting
the most efficient approach – pure MT, human review of the MT, or fully
human translation
• We will be happy to share our experience, technologies and tools :)

Thank you!
Jānis Ziediņš, janis.ziedins@kis.gov.lv
Andrejs Vasiļjevs, andrejs@tilde.com

Heli
Kautonen
Library Director
Finnish Literature Society SKS
Design for Diversity

Design for Diversity
Heli Kautonen
Library Director, Finnish Literature Society (SKS)
24.10.2019
Europeana meeting on multilingualism, Hanaholmen, Finland

1831
Photo © Gary Wornell, SKS 2019

Image © SKS 2010
Suomalaisen
Kirjallisuuden
Seura
(Finnish Literature Society)

Photo © Gary Wornell, SKS 2019

Photo: Alexandre Caffiaux, Université de Lille, 2018. CC-BY 2.0
Diversity

Diversity
Photo: Jackster121212 - Own work, CC BY-SA 4.0,
https://commons.wikimedia.org/w/index.php?curid=80077504C

Photo: Heli Kautonen 2017
Design

Universal Design
Critical Design
Inclusive Design
Value-Sensitive Design
Photo: Helsinki City Museum, CC-BY 4.0
Source: Finna.fi
Photo: newobj Source: Github.com

How might we…?

…measure
the value…
…now,
next year,
in the future?

Development Implementation Operation and maintenance Initiation
(of a new service)
time
Process-time Use-time Future
Who are involved in
the development and
implementation of
your service?
What kinds of benefits
can be identified?
Who uses your
service? Are there
other stakeholders?
can be identified?
Who could (re)use
your service or
materials in the
(undefined) future?
can be anticipated?
Model for temporal division of benefits
Kautonen, H. & Nieminen, M. (2018): Conceptualizing Benefits of User-Centered Design for Digital Library
Services. Liber Quarterly, 28(1), ss. 1–34. DOI: http://doi.org/10.18352/lq.10231.

TrustEfficiency
Revenue
Better
quality
Learning &
competence
Self
esteem
Ease
of use
Cost
savings
COMMITMENT
Sustainability

”for + with
society”
Prof. Linda Doyle
Trinity College Dublin

2031

Questions and comments
heli.kautonen@finlit.fi
Twitter: @helimuori
https://fi.linkedin.com/in/heli-kautonen-38136512

Dasha
Moskalenko
Manager Service Design
Europeana case study
UX Design and user testing

Ο Ζητιάνος Φοιτητής, Άγνωστος δημιουργός, 1945,Ίδρυμα Μουσείου Νίκου Καζαντζάκη, Greece, CC BY-NC-ND

Καντσονίσιμα-Σατιρίσιμα-Ψυθιρίσιμα, Άγνωστος δημιουργός, 1971, Ίδρυμα Μουσείου Νίκου Καζαντζάκη, Greece, CC BY-NC-ND

Language detection and display (for validation)Query translated in 24 languages

Results displayed based on relevance in all languagesResults displayed in original languageSearch term highlighted

Sort by language availableLanguage tag showing item’s original languageLanguages in which item metadata is available

Item’s original language & option for automatically translation

Hands showing the French sign language alphabet, Wellcome Collection, CC BY
europeana.eu
@EuropeanaEU
THANK YOU!
Questions & comments are welcome.
dasha.moskalenko@europeana.eu

Matias
Frosterus
Information Systems Manager
with Mikko Lappalainen, Osma Suominen,
Satu Niininen
National Library of Finland
Multilingual linked vocabularies
and automatic subject indexing
services - National Library's
Finto and Annif

THE NATIONAL LIBRARY OF FINLAND
Libraries and access

?

!

The goal
▪ Bringing the library know-how into use for all of the public
sector
▪ But better!
▪ Better vocabularies
▪ Publication, use, and integration of those better vocabularies
▪ Automated tools to make it even easier

What is needed?
▪ Modern linked data vocabularies
▪ A way to publish them for everyone to use
▪ A way to integrate them into your systems
▪ A way to make using them less labour-intensive

Vocabularies

Vocabularies
▪ Starting point: General Finnish Thesaurus YSA
▪ Developed in the 1980’s mainly for book indexing
▪ Over 30,000 terms
▪ Monolingual but has a Swedish counterpart Allärs

Thesaurus to ontology
▪ Reconstruction of YSA into machine-readable and multilingual YSO
▪ Trilingual terms for concepts (fin, swe, eng)
▪ YSA and Allärs merged together and translated into English
▪ Concepts are a compromise between Finnish and Swedish as YSA
and Allärs are not completely identical
▪ Links to Library of Congress Subject Headings (LCSH)
▪ Linking to Wikidata underway
▪ YSO just made the list of Europeana dereferenceable vocabularies
that can be enriched in the Europeana portal

Annotate in one language, find using another

Challenges of multilinguality
▪ Founded on the concepts of the Finnish cultural sphere
▪ Some concepts may not be common outside of that
▪ sandwich cakes, uncles (maternal)
▪ väheneminen = minskning (antal) = decrease (passive)
vähentäminen = minskning (aktiv reducering av antal) = decrease (active)
▪ Liikuntalukiot = idrottsgymnasier = general upper secondary schools
focusing on sport and exercise

▪ Some may result in somewhat awkward terms
▪ rivers = joet = floder, åar och älvar
▪ The original Swedish thesaurus Allärs had three terms that could be
used interchangeably

▪ Can also affect hierarchy
▪ pesät
⤷ muurahaispesät (literally ant nests)
bon
⤷ myrstackar
nests
⤷ ant hills
▪ For more information, see http://urn.fi/URN:NBN:fi-fe201705106375
Satu Niininen, Susanna Nykyri, Osma Suominen, (2017) "The future of
metadata: open, linked, and multilingual – the YSO case", Journal of
Documentation, Vol. 73 Issue: 3, pp.451-465, doi: 10.1108/JD-06-2016-0084.

YSO
YSO
Upper
hierarchy
General
concepts
Specific
concepts

Adapted into use outside the library domain
▪ Extended with domain ontologies
▪ Using the core provided by YSO
▪ Helps interoperability!
▪ Developed by the domain experts in various organizations

Adapted into use outside the library domain
▪ Extended with domain ontologies
▪ Using the core provided by YSO
▪ Helps interoperability!
▪ Developed by the domain experts in various organizations
▪ Over a dozen domain ontologies such as:
▪ AFO - Agriculture - 7 000 concepts
▪ JUHO - Government - 6 300
▪ KAUNO - Literature - 5 000
▪ KULO - Cultural research - 1 500
▪ LIITO - Economics - 3 000
▪ SOTO - Military - 2 000
▪ TERO - Health - 6 500
▪ And others

Domain ontologies all extending YSO in

KOKO
▪ An ”ontology cloud” which combines the domain ontologies
and the general ontology into a cohesive whole

Vocabulary service

National vocabulary and ontology service
Finto
▪ A bit of history
▪ FinnONTO-research project (2003-2012)
▪ Built research prototypes of services and started the ontologization
process of the various thesauri
▪ The National Library began the Finto project in 2013 funded by
the Ministry of Education and Culture and the Ministry of Finance
▪ A national vocabulary and ontology service for the whole public
sector

Finto offers

Finto offers
Free to use
Open licenses

Adopted widely in Finland
▪ Finto is used in many organizations in Finland to annotate
their various resources, among them
▪ The national broadcasting company Yle
▪ Suomi.fi citizen’s portal to public services
▪ Various public sector content systems
▪ Websites of various ministries
▪ Various museums, archives, and libraries

Skosmos
▪ The heart beating inside Finto
▪ Open source SKOS vocabulary browser
▪ http://skosmos.org
▪ Publication and use of light-weight ontologies, thesauri and classifications
▪ Web interface
▪ REST API
▪ SPARQL endpoint
▪ Community
▪ https://groups.google.com/forum/#!forum/skosmos-users

How does it work?
▪ Make your thesaurus into SKOS

SPARQL
▪ Put in in a SPARQL triple store
How does it work?

SPARQL
Skosmos
▪ Point Skosmos at your SPARQL endpoint
How does it work?

SPARQL
Skosmos
▪ And serve your thesaurus for
humans, Linked Data agents,
and REST API access
How does it work?

Key features
▪ Multilingual browser interface (10 languages)
▪ Autocomplete search
▪ Alphabetical index
▪ Concept hierarchy display
▪ Concept groups (thematic index)
▪ New concepts
▪ REST API for enabling use of vocabularies in other
applications
▪ responses usually JSON-LD

www.loterre.fr/skosmos
http://chemskos.com
Skosmos installations around the world
http://vocabularies.unesco.org/ http://aims.fao.org/standards/agro
voc/functionalities/search

Automated subject indexing

Many possible solutions

Some problems
YSO
KOKO
AFO
JUHO
€ £ $

Automated Subject Indexing made easy:
Annif
▪ An open source multilingual automated subject indexing
system using machine learning and our own vocabularies

Where to get the learning material?

Metadata about 13M documents,
many of them tagged with subjects! Hot tub by a lake
Andrei Niemimäki
CC BY-SA

Hot tub by a lake
Andrei Niemimäki
CC BY-SA
Metadata about 13M documents,
many of them tagged with subjects!

Finna API
▪ All Finna metadata is
▪ YSO and KOKO widely used

▪ Try it out for yourself at http://annif.org/
Annif
Prototype in 2017

Annif
VsAutomating our own processes Creating generic tools for many contexts

Annif development
▪ Packaging Annif into an easy-to-deploy solution via Docker
▪ Tuning the various algorithms and their hyperparameters
powering Annif
▪ Making integration easier through a Finto API

Summary

Summary
Interlinked multilingual vocabularies
for various domains
A national service for
publishing and using
said vocabularies
An automated system
for making it easy
to produce annotations
with said vocabularies

Summary
Interlinked multilingual vocabularies
for various domains
A national service for
publishing and using
said vocabularies
An automated system
for making it easy
to produce annotations
with said vocabularies
All the while
utilizing library
know-how
Richer metadata
Cross-domain findability and interoperability
More efficient workflows
New connections, new possibilities

Thank you!
matias.frosterus@helsinki.fi
finto-posti@helsinki.fi
@Fintopalvelu
All pictures used under CC0
license unless otherwise noted

Hugo
Manguinhas
Product Manager API
Case Study -
Translation of object metadata
using the Knowledge Graph

Multilingual experience
Collections
Object metadata
Text objects
Search
Browse
Display
Translatable dataUsage scenarios
Editorial content
User interface

Object Metadata
What is the title of the object?
Who created or contributed it?
What topics is the object about?
What kind of object it is?
When was it created or published?
Where was it created or is located?
...

KNOWLEDGE GRAPH
Bulong Miao, Wellcome Collection, United Kingdom, CC BY

About the Knowledge Graph
● Vast network of data sources made available in
the wider Linked Open Data cloud
● Can be linked to and used to bring more
contextual information to the items
● Vast and readily available source of controlled
translations
Part of the Linking Open (LOD) Data Project Cloud Diagram, CC-BY-SA.

EDM and the Knowledge Graph
We encourage data providers to
● Contribute links to their own
vocabularies and publish them as
Linked Open Data
● Use available reference vocabularies
to describe their content
Clavecin, Bartolomeo Cristofori
Cite de la Musique,
MIMO - Musical Instruments Museums
Online|CC BY-NC-SA

● Available as Linked Open Data and
therefore part of the Knowledge Graph
● The rights statements have been
translated into: Estonian, Finnish,
French, German, Polish and Spanish,
but 7 more translation efforts are
ongoing
Research has shown that the
official translation of rights
information leads to better
investment/effort into adoption
of rs.org and thus more accurate
copyright info

General Finnish Ontology (YSO)
<skos:Concept rdf:about="http://www.yso.fi/onto/yso/p4349">
<skos:prefLabel xml:lang="sv">hederstecken</skos:prefLabel>
<skos:prefLabel xml:lang="fi">kunniamerkit</skos:prefLabel>
<skos:prefLabel xml:lang="en">medals of honour</skos:prefLabel>
<skos:altLabel xml:lang="sv">ordnar</skos:altLabel>
<skos:altLabel xml:lang="sv">ordnar (hederstecken)</skos:altLabel>
<skos:broader rdf:resource="http://www.yso.fi/onto/yso/p1581"/>
<skos:related rdf:resource="http://www.yso.fi/onto/yso/p4347"/>
<skos:exactMatch rdf:resource="http://www.yso.fi/onto/koko/p30868"/>
<skos:exactMatch rdf:resource="http://www.yso.fi/onto/ysa/Y96541"/>
<skos:exactMatch rdf:resource="http://www.yso.fi/onto/allars/Y23916"/>
</skos:Concept>

Vocabularies used by Data Providers
language coverage: 0.36
(topics and subjects)
Not all vocabularies are
properly language tagged!

Europeana’s Knowledge Graph
Entity
Collection

Entity Collection: beneﬁts
● Allows Europeana to establish links to the
Knowledge Graph through means of semantic
enrichment of the object metadata
● Harmonizes vocabularies from the multiplexity of
data providers into a single point of reference
● Exploits coreference links between vocabularies
to increase multilingual coverage
Entity
Collection
Entity
Collection

Entity Collection: multilingual coverage
language coverage: 13.1
(topics and subjects)
For persons drops to 4.8

Entity Collection: multilingual improvements

Steps to improve the Knowledge Graph
● Promote alignment efforts
between vocabularies used by
data providers to complementary
vocabularies such as Wikidata
● Promote translation
efforts/campaigns to increase
multilingual coverage of the
Knowledge Graph prioritising on
discovery-enabling metadata
fields

A FOCUSED VIEW ON
THE GENERAL STRATEGY
Idrottstävlingar på Eyravallen. "Benke". 27 september 1955.,Örebro Kuriren, Örebro läns museum, Sweden, Public domain

Multilingual search, browse and display
Usage scenarios
● Enter search query in chosen language
● See search results and interact with ﬁlters in
chosen language
● Display object metadata on item page
● Navigate to entities

Proposals for indexing and storing translations
● Automated identification of language if needed (only 26.5% of the data
provider’s metadata is language qualified)
● Use translations from multilingual knowledge graph
● Augment the provider metadata with static translation of the fields to English
(to fill metadata values not covered by the knowledge graph)
● Store and index translated metadata for search and display (original metadata
+ languages of the knowledge graph + English)

Proposals for search on object metadata
Identify
language
Original
query
Translate to
English
Multilingual
index
User
Disambiguates
Search
Translated query (English)
Suggest Entity
(Knowledge Graph)
Entity-based query
Multilingual query:
entity based query
OR original query +
translated query
#1: French
#2: Spanish
#3: Polish

Proposals for display of object metadata
Multilingual
Database
Translate from
English
Obtain metadata
(Knowledge Graph)
In original language
or English
Obtain
metadata
In other
language
Request
metadata

MULTILINGUAL EXPERIENCE
OUTCOMES
● Users can search and filter in one of 24 official languages
● Item page metadata would display in chosen language if knowledge
graph translations were present
● Where chosen language is not supported, display will default to
source language and offer option to view in English

Challenges & Open Questions
● How successful is automated language detection?
● Would prioritising static translation of discovery-enabling metadata ﬁelds to
English be “good enough”?
● How well can we statically translate remaining metadata ﬁelds to English,
specially when they contain single or short phrases?
● Would dynamic translation of metadata (for languages other than English) be
good enough?

The Chinese Market, 1767 - 1769, Rijksmuseum, Netherlands, Public domain
europeana.eu
@EuropeanaEU

Europeana meeting under Finland’s Presidency of the Council of the EU - Day 1, 24 october 2019

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