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FREME Business Cases – FREME at FEISGILTT 2015 WWW.FREME-PROJECT.EU 1 Co-funded by the Horizon 2020 Framework Programme of the European Union Grant Agreement Number 644771 FEISGILTT 2015 |BERLIN, 2 JUNE 2015 Felix Sasaki, DFKI / W3C Fellow On behalf of the FREME Consortium FREME BUSINESS CASES www.freme-project.eu
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FREME BUSINESS CASES...FREME Business Cases – FREME at FEISGILTT 2015 1 Co-funded by the Horizon 2020 Framework Programme of the European Union Grant Agreement Number 644771 FEISGILTT

Aug 31, 2020

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Page 1: FREME BUSINESS CASES...FREME Business Cases – FREME at FEISGILTT 2015 1 Co-funded by the Horizon 2020 Framework Programme of the European Union Grant Agreement Number 644771 FEISGILTT

FREME Business Cases – FREME at FEISGILTT 2015 WWW.FREME-PROJECT.EU 1

Co-funded by the Horizon 2020 Framework Programme of the European Union Grant Agreement Number 644771

FEISGILTT 2015 |BERLIN, 2 JUNE 2015

Felix Sasaki, DFKI / W3C Fellow

On behalf of the FREME Consortium

FREME BUSINESS CASES

www.freme-project.eu

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THE FREME PROJECT

• Two year H2020 Innovation action; start February 2020

• 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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GROUPING OF PARTNERS

• Technology solution providers

◦ DFKI, InfAI, iMinds, Tilde

• Business Case Partners

◦ iMinds, VistaTEC, Agro-Know, Wripl

• Dissemination and Business Modeling

◦ ISMB

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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

FR

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, translator, …

* Graphical interfaces * Software Interfaces

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FREME E-SERVICES – BIRDS EYE VIEW

• e-Entity

◦ Automatic annotation of named entities

• e-Terminology

◦ Annotation of terms and linkage to term databases

• e-Link

◦ Enrichment with information from (linked) (open) data sources*

• e-Translation

◦ Cloud based machine translation

• e-Internationalisation

◦ ITS 2.0 metadata to govern the multilingual & semantic content workflow

• e-Publishing

◦ Publish enriched content in ePub format

* More on this: see presentation FEISGILTT 4 June

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BUSINESS CASES

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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

◦ Input: “Welcome to Berlin … Marlene Dietrich!”

◦ Output:

[ { "@id": "dbpedia:Marlene_Dietrich", "@type": "person", "born": "1901-12-27" }, { "@id": "dbpedia:Berlin", "@type": "place", "population": "3746444" }

]

May be basis e.g. for 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

• More tomorrow = FEISGILTT day 2, during the “linked data” presentation

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DEMO: ENRICHMENT IN OXYGEN XML EDITOR DISCLAIMER: NOT YET FREME BASED, BUT SHOWS THE POSSIBILITIES OF COMBINING E-ENTITY AND E-LINK

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LESSONS TO LEARN FOR PROJECTS INVOLVING ENRICHMENT

• Adequate interfaces for the user type in question are key

◦ E.g. content creator vs. translator

• Hide complexity of technologies in a user specific way

◦ Ability to adapt linked data queries without knowing linked data – “I want to find information about cities and related tourists sites in the radius of X miles”

• New job roles may be needed

◦ Today: content architect, set up of content workflows

◦ With FREME: enrichment architect, set up of language and data tooling (e.g. preparing linked data queries)

• FREME may also help to automatize the collaboration between departments (e.g. localisation <> marketing) within one organisation

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CHALLENGES

• Technical, e.g.: How to store enrichment information in the format being enriched?

◦ RDF as storage and processing format not widely deployed e.g. in multilingual content creation

• Closing knowledge gaps

◦ Sharing knowledge between different types of tool developers: language technology, linked data, localisation, content creation, …

• Aspects of data sets

◦ Availability for commercial purposes, quality, sustainability, complexity of the data (“How do I find X?”)

• Qualification & tooling

◦ Teaching translators just enough linked data to do proper enrichment

• Convincing (end) customers: developing killer applications using FREME

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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

Idea: discuss concrete standardisation opportunities at a W3C workshop on interfaces to LT & LD technologies? Ping me if interested

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CONTACTS

FELIX SASAKI

Senior Researcher DFKI / W3C Fellow

On behalf of the FREME consortium

E-mail: [email protected]

CONSORTIUM