The FREME project is a two-year Horizon 2020 project that aims to bridge language and data technologies. It involves industry partners leading four business cases around digital content and linked data. The project will develop a set of interfaces for language and data technologies to allow users like content authors and developers to easily integrate these technologies. The interfaces will be designed based on the needs of the business cases in domains like publishing, translation, agriculture, and personalization.

1. FREME – General overview. Version June 2015 WWW.FREME-PROJECT.EU 1
Co-funded by the Horizon 2020
Framework Programme of the European Union
Grant Agreement Number 644771
VERSION: JUNE 2015
Presented
on behalf of the FREME Consortium
FREME – GENERAL OVERVIEW
www.freme-project.eu

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THE FREME PROJECT
• Two year H2020 Innovation action; start February 2015
• Industry partners leading four business cases around
digital content and (linked) data
• Technology development bridging language and data
• Outreach and business modelling demonstrating monetization of the multilingual
data value chain

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CHALLENGE AND OPPORTUNITY: BIG DATA IS GROWING ACROSS
LANGUAGES, SECTORS AND DOMAINS
• BC: Digital publishing
• BC: Translation and localisation
• BC: Agriculture and food domain data
• BC: Web site personalisation
Agriculture
metadata, user
content, news
content, …
WHAT LIES AHEAD FOR SEVERAL INDUSTRIES? SEE THE FREME BUSINESS CASES
EN
ES
JA, ZH, ...
AR

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CURRENT STATE OF SOLUTIONS
Machine
translation,
terminology
annotation, ...
Linked data
creation &
processing
GAPS THAT HINDER BUSINESS:
• Plethora of formats
• Adaptability and platform dependency
• Language coverage
• Usability “The right tool for the right person
in given and new enterprises”:
technology influences job profiles

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FREME TO THE RESCUE: ENRICHING DIGITAL CONTENT
Machine
translation,
terminology
annotation, ...
Linked data
creation &
processing
LT and LD as first class
citizens on the Web
A SET OF INTERFACES* - DESIGN DRIVEN
BY BUSINESS CASES
LT and LD for various
user types: (application)
developer, content
architect, content
author, …
* Graphical interfaces
* Software Interfaces

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EACH SERVICE IN ONE SENTENCE
• e-Translation: “Translate from Dutch to English”
• e-Terminology: “Add terminology annotations”
• e-Entity: “Identify unique entities”
• e-Link: “Add information from (linked open) data sources”
• e-Publishing: “Publish as digital book content”
• e-Internationalisation: “Use standardised metadata for multilingual content
production”
A KEY ASPECT FREME: FREME will allow to combine data and language technologies via
adequate software interfaces (APIs) and graphical user interfaces (GUIs)

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AUTHORING AND PUBLISHING MULTILINGUALLY AND SEMANTICALLY
ENRICHED EBOOKS
• Challenge
◦ Moving to digital publishing workflows leads to high costs. Added value beyond the content
itself is needed.
• Solution
◦ Easy to integrate solutions, based on standardised interfaces for software components and
authors, responding to usability requirements

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AUTHORING AND PUBLISHING MULTILINGUALLY AND SEMANTICALLY
ENRICHED EBOOKS
• Example: Integration into ePub editing mode of oXygen XML Editor
e-Entity: annotate named entities

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INTEGRATING SEMANTIC ENRICHMENT INTO MULTILINGUAL
CONTENT IN TRANSLATION AND LOCALISATION
• Challenge
◦ Localisation service providers are under pressure: more speed, better quality, lower
payment rates per translation outcomes. LSPs need to offer additional services, going
beyond translation
• Solution
◦ Allow translators to work with linked data sources during the translation process, to add
additional information to the translated content.

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INTEGRATING SEMANTIC ENRICHMENT INTO MULTILINGUAL
CONTENT IN TRANSLATION AND LOCALISATION
• Example: Integration into XLIFF 2.0 editing mode of oXygen XML Editor
• Combination of services
◦ e-Entity: annotate named entities
◦ e-Link: fetch additional information from a linked data source like dbpedia, specific to the
type of entities (places, persons, …)

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INTEGRATING SEMANTIC ENRICHMENT INTO MULTILINGUAL
CONTENT IN TRANSLATION AND LOCALISATION
• Enriching content with machine readable information – represented as JSON-LD
◦ Input: “Welcome to Berlin … Marlene Dietrich!”
◦ Output:
[
{
"@id": "dbpedia:Marlene_Dietrich",
"@type": "person",
"born": "1901-12-27"
}
]
May be basis e.g. for further processing, e.g.
multilingual generation:
• “… born 1901”
• “… geboren 1901”
• “…１９０１年生まれ”
• …

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ENHANCING THE CROSS LANGUAGE SHARING AND ACCESS TO OPEN
AGRICULTURAL AND FOOD DATA
• Challenge
◦ Content metadata is not available in multiple languages, needs to be curated manually, and
the metadata is not linked to external data sources.
• Solution
◦ Via FREME, make metadata available in several languages
◦ Easy data discovery for users with different linguistic profiles
◦ Allow to reach out to new markets

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FREME-EMPOWERED PERSONALISED CONTENT RECOMMENDATIONS
• Challenge
◦ Personalised content recommendations for content rich websites help to increase
engagement of users on a website. Current solutions focuses on English web sites.
• Solution
◦ Using FREME, content recommendation will be provided in several languages
◦ The e-Services e-Entity and e-Translation will help to identify user relevant items in Web
content

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WANT TO TRY THINGS OUT?
• Go to http://api.freme-project.eu/doc/0.1/
• Check out API demo calls
• Time line for next prototypes
◦ 0.2: mid July
◦ 0.3: end of August
◦ Feedback to GitHub: https://github.com/freme-project
- Will be made public repro mid July

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NEXT STEPS FOR FREME
Creating & updating prototypes of e-Services
Agile approach: gathering feedback from business cases feeds implementations
Potential input to standardisation of the open Web platform: ease access to
data (sources + technologies) and language technologies for various user types
Learn more at www.freme-project.eu
Get input from users outside the FREME consortium

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CONTACTS
On behalf of the FREME consortium
E-mail: info@freme-project.eu
CONSORTIUM