News

03 JUL 2020

Coreon MKS as LLOD is European Language Grid top funded project

Coreon’s proposal for using the European Language Grid (ELG) as a platform for making multilingual interoperability assets discoverable and retrievable has been awarded. This will be achieved by complementing Multilingual Knowledge Systems with a SPARQL interface. The ELG Open Call 1 received 121 proposals, of which 110 were eligible and 10 were selected. Coreon’s proposal “MKS as Linguistic Linked Open Data” was amongst the three winning proposal from industry and received the highest funding.

The goals of the project are a) to enable Semantic Web systems to query Coreon’s richly elaborated multilingual terminologies stored in concept systems and knowledge graphs…
Coreon’s proposal for using the European Language Grid (ELG) as a platform for making multilingual interoperability assets discoverable and retrievable has been awarded. This will be achieved by complementing Multilingual Knowledge Systems with a SPARQL interface. The ELG Open Call 1 received 121 proposals, of which 110 were eligible and 10 were selected. Coreon’s proposal “MKS as Linguistic Linked Open Data” was amongst the three winning proposal from industry and received the highest funding.

The goals of the project are a) to enable Semantic Web systems to query Coreon’s richly elaborated multilingual terminologies stored in concept systems and knowledge graphs and b) to prove how to overcome the limits of RDF/knowledge graph editors, which usually are fine to model concept relations, but are weak in capturing linguistic information. When deployed in March 2021 on the ELG, the innovation will enable the Semantic Web community to query rich multilingual data with a familiar, industry standard syntax.
07 NOV 2019

CEFAT4Cities Action Gets Funding

The CEFAT4Cities Action, to be executed by a multinational consortium of five partners, led by CrossLang, has received funding. The action starts in April 2020 and runs up to March 2022.
The main objective of the CEFAT4Cities Action is to develop a “Smart cities natural language context”, providing multilingual interoperability of the Context Broker DSI and making public “smart city” services multilingual, with pilots in Vienna and Brussels.
The language resources that will be created will be committed to the ELRC repository and the following languages will be developed: Dutch, English, French, German, Italian, Slovenian, Croatian and Norwegian.

Coreon's…
The CEFAT4Cities Action, to be executed by a multinational consortium of five partners, led by CrossLang, has received funding. The action starts in April 2020 and runs up to March 2022.
The main objective of the CEFAT4Cities Action is to develop a “Smart cities natural language context”, providing multilingual interoperability of the Context Broker DSI and making public “smart city” services multilingual, with pilots in Vienna and Brussels.
The language resources that will be created will be committed to the ELRC repository and the following languages will be developed: Dutch, English, French, German, Italian, Slovenian, Croatian and Norwegian.

Coreon's role in the consortium is provide the appropriate technology, to turn vocabularies into multilingual knowledge graphs, to curate and extend them to model the domain of smart cities.
18 MAY 2021

The Journey to a Multilingual SPARQL Endpoint

The idea of accessing a Multilingual Knowledge System through the means and methods of the Semantic Web brings two keywords immediately to mind: SPARQL and LOD (Linked Open Data). We’ve already talked about the benefits of doing this and how it enables your enterprise to handle all its data via one, centralized hub in a previous post, but how did we actually achieve it?

The Coreon MKS is powered by a RESTful Web API, sending its data in JSON data structures through the wire. Everyone can develop extensions and custom solutions based on this. We ourselves did it recently, for…

The idea of accessing a Multilingual Knowledge System through the means and methods of the Semantic Web brings two keywords immediately to mind: SPARQL and LOD (Linked Open Data). We’ve already talked about the benefits of doing this and how it enables your enterprise to handle all its data via one, centralized hub in a previous post, but how did we actually achieve it?

The Coreon MKS is powered by a RESTful Web API, sending its data in JSON data structures through the wire. Everyone can develop extensions and custom solutions based on this. We ourselves did it recently, for instance: creating a plug-in to SDL Trados Studio so that linguists can directly access information stored in Coreon.

However, this required the developer of the plug-in to get familiar with the API and its data structures.

In the world of the Semantic Web (aka ‘the web of data’), we no longer see proprietary APIs. Developers and integrators instead access all the resources through the same method – SPARQL. Wouldn't it be great to also access the Coreon repositories via a SPARQL endpoint?

We will outline how we did it with Coreon, but the process is not only relevant for our own MK system – it could easily act as a blueprint or guideline for those working with similar tools.

JSON Structures to RDF Graph

The first step was to analyze how Coreon's data model could be mirrored in a RDF graph. What were the information objects? What were the predicates between them? What showed up as a literal?

In RDF, all elements or pieces of information you want to "talk about" are good candidates for becoming objects or, technically speaking, OWL classes. There were obvious candidates for classes, namely Concept or Term, but how about the concept relations such as “broader” or custom associative ones like “is-complementary-to”? How about descriptive information such as a Definition or Term Status value? Concretely, we had to go from the JSON data structure to an RDF graph model.

Before we dive in deeper, here’s a sample concept (with ID: 607ed17b318e0c181786b545) in Coreon that has two terms, English screen and German Bildschirm. Notice also the individual IDs of each of the terms – they will become important later on.

Coreon concept example with concept and term ids shown

In the original JSON data structure, this concept is represented as follows (only relevant code lines shown):

{
    "created_at": "2021-04-20T13:04:59.816Z",
    "updated_at": "2021-04-20T13:05:25.856Z",
    "terms": [
        {
            "lang": "en",
            "value": "screen",
            "created_at": "2021-04-20T13:04:59.816Z",
            "updated_at": "2021-04-20T13:04:59.816Z",
            "id": "607ed17b318e0c181786b549",
            "concept_id": "607ed17b318e0c181786b545",
            "properties": [],
            "created_by": {
                "email": "michael.wetzel@coreon.com",
                "name": "Michael Wetzel"
            },
            "updated_by": {
                "email": "michael.wetzel@coreon.com",
                "name": "Michael Wetzel"
            }
        },
        {
            "lang": "de",
            "value": "Bildschirm",
            "created_at": "2021-04-20T13:05:25.856Z",
            "updated_at": "2021-04-20T13:05:25.856Z",
            "id": "607ed195318e0c181786b55e",
            "concept_id": "607ed17b318e0c181786b545",
            "properties": [],
            "created_by": {
                "email": "michael.wetzel@coreon.com",
                "name": "Michael Wetzel"
            },
            "updated_by": {
                "email": "michael.wetzel@coreon.com",
                "name": "Michael Wetzel"
            },
        }
    ],
    "id": "607ed17b318e0c181786b545",
    "coreon_type": null,
    "alias": false,
    "referent_id": null,
    "branch_root": false,
    "properties": [],
    "parent_relations": [
        {
            "concept_id": "606336dab4dbcf018ed99308",
            "type": "SUPERCONCEPT_OF"
        }
    ],
    "child_relations": []
}

When we transform this into an RDF graph, the concept and its two terms are bound together in statements (so-called triples), each consisting of a subject, a predicate and an object. The concept will act as the subject, the term(s) act as the object(s), and the required predicate could be named in this case: hasTerm. This gives us the following triple:

coreon:607ed17b318e0c181786b545 coreon:hasTerm coreon:607ed17b318e0c181786b549 .

The triple shows that the resource with the ID 607ed17b318e0c181786b545 contains a term, and the term’s ID is 607ed17b318e0c181786b549. It doesn't yet say anything about the value or the language of the term. It simply states that the term with the given ID is a member of that concept.

Now the next triple shows that the value for the resource with ID 607ed17b318e0c181786b549 has a literal value in English, namely the string screen:

coreon:607ed17b318e0c181786b549 coreon:value “screen”@en .

Such a set of triples, i.e. many atomic statements bound together via predicates, make up the RDF graph. If we visualize some of these triples, the resulting RDF graph looks like this:

Representing concepts and terms as an RDF graph

Concepts and terms are classes (in green and blue), predicates are graph edges (above the lines).

The complete set of triples would be serialized as follows in RDF / Turtle:

@prefix coreon: <http://www.coreon.com/coreon-rdf#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://www.coreon.com/coreon-instance> a owl:Ontology;
  owl:imports <http://www.coreon.com/coreon-rdf>;
  owl:versionInfo "Created through Coreon export" .

coreon:607ed17b318e0c181786b547 a coreon:Edge;
  coreon:edgeSource coreon:606336dab4dbcf018ed99308;
  coreon:edgeTarget coreon:607ed17b318e0c181786b545;
  coreon:type "SUPERCONCEPT_OF" .

coreon:606336dab4dbcf018ed99307 a coreon:Term;
  coreon:value "peripheral device"@en .

coreon:606336dab4dbcf018ed99308 a coreon:Concept;
  coreon:hasTerm coreon:606336dab4dbcf018ed99307 .

coreon:607ed17b318e0c181786b545 a coreon:Concept;
  coreon:hasTerm coreon:607ed195318e0c181786b55e,
    coreon:607ed17b318e0c181786b549 .

coreon:607ed17b318e0c181786b549 a coreon:Term;
  coreon:value "screen"@en .

coreon:607ed195318e0c181786b55e a coreon:Term;
  coreon:value "Bildschirm"@de .

You may also have noticed in the above syntax that two or more statements are serialized together – separated via semicolons and ending with a full stop. Line 18 indicates that the resource with the ID 606336dab4dbcf018ed99308 is of OWL class coreon:Concept, and line 19 further indicates that it contains a term which has the ID 606336dab4dbcf018ed99307.

No RDF without URIs

Now all the pieces of information are bound together via RDF statements: the triples. They have a pretty atomic, isolated nature. This is quite different to how XML and other standard formats organize information. In RDF and LOD all data is stored in this atomic manner, uniquely identifiable through the URI.

Via the URIs and the predicates such as hasTerm , the resources are bound together. Only then does it become meaningful for an application or a human, as the URIs are an indispensable prerequisite. All information elements that are represented as classes have unique identifiers. The namespace coreon: , together with the unique IDs, unambiguously identifies a given resource. This is regardless of whether it is a concept, term, property, or even a concept relation. Fortunately we stored all data with URIs when we created the fundamental design of Coreon. Phew.

Build the Coreon RDF Vocabulary

After researching the basic approach described above, we analyzed all elements of the Coreon data structure and rethought them as a member of our RDF vocabulary. The following table lists the most important ones:

OWL Type Coreon RDF Vocabulary
Classes owl:Class coreon:Admin, coreon:Edge, coreon:Concept, coreon:Flagset, coreon:Property, coreon:Term
Predicates owl:ObjectProperty coreon:hasAdmin, coreon:hasFlagset, coreon:hasProperty, coreon:hasTerm
Values owl:AnnotationProperty coreon:edgeSource, coreon:edgeTarget, coreon:id, coreon:name, coreon:type, coreon:value

For the predicates we also specified what kind of information can be used, defining owl:range and owl:domain. For instance, the predicate hasTerm can only accept resources of type coreon:Concept as a subject (owl:domain). As an example: the full specification of the predicate hasTerm looks as follows:

coreon:hasTerm
  rdf:type owl:ObjectProperty ;
  rdfs:comment "makes a term member of a concept" ;
  rdfs:domain coreon:Concept ;
  rdfs:label "has term" ;
  rdfs:range coreon:Term .

Publish as an Offline Resource

Once our RDF vocabulary was ready, the first step to implement it into Coreon was to add an RDF publication mechanism to the export engine. Equipped with this, Coreon can now export its repositories in RDF, including various syntaxes (Turtle, N3, JSON-LD and more).

Real-Time Access via a SPARQL Endpoint

The final yet most complicated step was to equip the Coreon cloud service with a real-time accessible SPARQL endpoint. We chose Apache Fuseki. It runs as a secondary index in parallel to a repository's data store, updated in real-time. Thus any change a data maintainer makes is immediately accessible via the SPARQL endpoint!

Let me illustrate the ease and power of SPARQL with some examples...

Example 1: Query All the Definitions via SPARQL:

SELECT *
   WHERE {
       ?p rdf:type coreon:Property .
       ?p coreon:key "Definition" .
       ?p coreon:value ?v.
    }

We are querying all the objects that are of type coreon:Property (line 3) that also have a key with the name Definition (line 4). This is bound against the result variable p, and then for all these we retrieve the values, which are bound against the variable v.

A typical result table (here from a repository dealing with wine grape varieties) looks as follows:

[p] v
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8a1 Chardonnay is the most famous and most elevated grape in the region of Northern Burgundy in ...
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8ab A white grape variety which originated in the Rhine region. Riesling is an aromatic grape variety ...
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8cd Pinot noir (French: [pino nwa?]) is a red wine grape variety of the species Vitis vinifera. The ...
... ...
... ...

The first column, representing the variable p, holds the URI of the property; the second column holds the literal value.

Example 2: Query all terms and - if present - also print their usage value:

A more realistic query compared to Example 1: get me all the terms and if they have a usage flag, such as preferred, print it, too.

SELECT ?t ?termvalue ?usagevalue
    WHERE {
        ?t rdf:type coreon:Term .
        ?t coreon:value ?termvalue .
        OPTIONAL {
            ?t coreon:hasProperty ?p .
            ?p coreon:key "Usage" .
            ?p coreon:value ?usagevalue .
        }
    }

A typical result might look as follows:

[t] termvalue usagevalue
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8aa Riesling
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8bb Cabernet Sauvignon Preferred
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8be CS Alternative
http://www.coreon.com/coreon-rdf#5f9ee3609323c01c4728b8c2 Merlot
...

This output table shows the term's URI, then its value, and - if available - the usage recommendation.

Example 3: How many Definitions are in your repository?

A last one to share...do you know how many Definitions or Comments are in your repository, or which are the most used properties? Well, how about this...

SELECT ?k (COUNT(?k) AS ?count)
{
	?uri coreon:key ?k.
}
GROUP BY ?k
ORDER BY DESC(?count)

...which delivers a table looking like this...nice!

k count
concept status 13806
usage status 10532
part of speech 10408
term type 10353
definition 5996

European Language Grid and Outlook

We thank the European Language Grid (ELG) for funding substantial parts of this development. It is a significant step and showcases how to complement software for multilingual knowledge with an open SPARQL / LOD access mechanism. The SPARQL endpoint is available to all Coreon customers. A selected set of demo repositories will also be accessible with the SPARQL endpoint through the ELG hub by summer 2021.

We are sharing our experiences with ISO / TC37 SC3 working groups, as a draft for a technical recommendation of how to represent TBX (TermBase eXchange) as RDF. Many of our findings in this journey towards a SPARQL endpoint can be used as a base for an international standard.

Like to know how multilingual knowledge can help your enterprise?

The post The Journey to a Multilingual SPARQL Endpoint appeared first on The Multilingual Knowledge Blog.

25 MAR 2021

Multilingual Knowledge for the Data-Centric Enterprise

Knowledge graphs are becoming a key resource for global enterprises. The textual labels of a graph’s nodes form a standardized vocabulary. Unfortunately, knowledge solutions are often wastefully developed in parallel within the same organization, be it in different departments or national branches. Starting from zero, domain experts build application-specific vocabularies in a hard-to-use taxonomy or thesaurus editor, mostly only in one language. Yet enterprise terminology databases in many languages are almost always already available. Enterprises and organizations simply have to realize that they can use this treasure chest of data for more than just documentation and translation processes.

Mutual understanding:

Knowledge graphs are becoming a key resource for global enterprises. The textual labels of a graph’s nodes form a standardized vocabulary. Unfortunately, knowledge solutions are often wastefully developed in parallel within the same organization, be it in different departments or national branches. Starting from zero, domain experts build application-specific vocabularies in a hard-to-use taxonomy or thesaurus editor, mostly only in one language. Yet enterprise terminology databases in many languages are almost always already available. Enterprises and organizations simply have to realize that they can use this treasure chest of data for more than just documentation and translation processes.

Mutual understanding: RDF and SPARQL

The problem is that legacy terminology solutions have proprietary APIs or special XML export formats. They also do not structure their concepts in a knowledge graph, which makes it hard to use them for more than translation. Taxonomies, thesauri, or ontology products, on the other hand, don’t cater for cross-language use and thus remain local. Multilingual Knowledge Systems such as Coreon bridge this gap, but until now even this also required integration through proprietary interfaces, or the exporting of data.

Multilingual knowledge unlocks true intelligence for the international enterprise
(App-Centric vs Data-Centric by cinchy).
Multilingual knowledge unlocks true intelligence for the international enterprise
(App-Centric vs Data-Centric by cinchy).

SPARQL (the recursive acronym for SPARQL Protocol and RDF Query Language) makes it possible to query knowledge systems without having to study their APIs or export formats. Coreon was therefore recently equipped with a SPARQL endpoint. Its knowledge repositories can now be queried in real time using the SPARQL syntax, i.e. a universal language for developers of data centric applications. 

Enterprises and organizations simply have to realize that they can use this treasure chest of data for more than just documentation and translation processes.

Semantic success

A central Multilingual Knowledge System, exposing its data via a SPARQL endpoint, thus becomes a common knowledge infrastructure for any textual enterprise application. This is regardless of department, country, or use case. For example: content management, chatbots, customer support, spare part ordering, or compliance can all be built on the same, normalized enterprise knowledge. In taking proprietary APIs out of the equation and with no need to export, mirror, and deploy into separate triple stores, real time access of live data is guaranteed.

Your organization already possesses this data. It’s just a case of maximizing its potential, introducing a cleaner and more accessible way of handling it. Contact us at info@coreon.com if you’d like to know more about how a common knowledge infrastructure can help your enterprise.

Coreon would like to extend special thanks to the European Language Grid, which funded significant parts of this R & D effort. The SPARQL endpoint will also be deployed into the ELG hub, so it will be reachable and discoverable from there.

The post Multilingual Knowledge for the Data-Centric Enterprise appeared first on The Multilingual Knowledge Blog.

11 JAN 2021

Keeping Your Sanity with Machine Taxonomization

Taxonomies are crucial for businesses and institutions to handle bigger amounts of data. Manually organizing thousands of concepts into a knowledge tree has so far been the only way to do this. Aside from the fact that this task can be quite tedious, it requires in-demand subject matter experts to complete. Thus, it is often considered too expensive or too much effort. A shame, given that companies then miss out on all the benefits of using taxonomies.

With a little help from your (AI) friend

Imagine a chaotic pile of books (of course, the less-organized among us may not have…

Taxonomies are crucial for businesses and institutions to handle bigger amounts of data. Manually organizing thousands of concepts into a knowledge tree has so far been the only way to do this. Aside from the fact that this task can be quite tedious, it requires in-demand subject matter experts to complete. Thus, it is often considered too expensive or too much effort. A shame, given that companies then miss out on all the benefits of using taxonomies.

With a little help from your (AI) friend

Imagine a chaotic pile of books (of course, the less-organized among us may not have to imagine this) being automatically sorted into shelves, branches, and sub-branches, together with an index to help quickly find a desired book. This describes what our semi‑automatic taxonomization method can do. An initial knowledge tree is produced by Machine Learning (ML), using language models stored in huge neural networks. Clustering algorithms on top of word embeddings automatically converts a haystack of concepts into a structured tree. The final curation of the taxonomy is still carried out by a human, but the most time-consuming and tedious aspects of the task have already been dealt with, and in a consistent way.

‘Cobot’ versus manual

In a study, we benchmarked this collaborative robot approach (ML auto‑taxonomization and human curation) against the manual job done by an expert linguist. Below are the data and task flows of the two approaches:

We aimed to taxonomize 424 concepts related to COVID-19. The traditional manual method was tedious and tiring for the human expert, who took a flat list of concepts and turned them into a systematic knowledge graph by working concept by concept to get everything in its right place. Wading through the list from scratch (including constantly switching contexts – from drugs, to vaccines, to social distancing, for example) made progress on the task difficult to measure. Having no perception of how many clusters of concepts still needed to be created was demotivating.

In contrast, our semi-automatic method started off with a tree of 55 suggested clusters of leaf concepts, each representing a specific context. Of course, ML doesn’t always produce the exact results a human expert would (we hear you, AI skeptics!), so some algorithm-suggested clusters were a bit off. However, the majority of the 55 were pretty accurate. They were ready to be worked on in Coreon’s visual UI, making the human curation task much faster and easier. This also enabled progress to be measured, as the job was done cluster by cluster.

By dramatically lowering the effort, time, and money needed to create taxonomies, managing textual data will become much easier and AI applications will see a tremendous boost.

Advantage, automation!

From a business perspective the most important result was that the semi‑automatic method was five(!) times faster. The structured head-start enabled the human curator to work methodically through the concepts. The clustered nature of the ML‑suggested taxonomy would also allow the workload to be distributed – e.g., one expert could focus on one medicine, another on public health measures.

More difficult to measure (but nicely visible below) was the quality of the two resulting taxonomies. While our linguist did a sterling job working manually, the automatic approach produced a tidier taxonomy which is easier for humans to explore and can be effectively consumed by machines for classification, search, or text analytics. Significantly, as the original data was multilingual, the taxonomy can also be leveraged in all languages.

A barrier removed

So, can we auto-taxonomize a list of semantic concepts? The answer is yes, with some human help. The hybrid approach frees knowledge workers from the tedious work in the taxonomization process and offers immediate benefits – being able to navigate swiftly through data, and efficient conceptualization.

Most importantly, though, linking concepts in a knowledge graph enables machines to consume enterprise data. By dramatically lowering the effort, time, and money needed to create taxonomies, managing textual data will become much easier and AI applications will see a tremendous boost.

If you’d like to discover more about our technology and services on auto-taxonomization, feel free to get in touch with us here

The post Keeping Your Sanity with Machine Taxonomization appeared first on The Multilingual Knowledge Blog.

9 DEC 2020

Making Translation GDPR-Compliant

Current processes violate GDPR

Out of the six data protection principles, translation regularly violates at least four: purpose limitation, data minimization, storage limitation, and confidentiality. This last one is most likely mentioned in most purchase orders, but it is hard to live up to in an industry which squeezes out every last cent in a long supply chain. 

Spicier is the fact that translators don’t need to know any personal data to translate a text, like who made the payment and how much money was transferred, as in the sample below. Anonymized source texts would address purpose limitation and…

Current processes violate GDPR

Out of the six data protection principles, translation regularly violates at least four: purpose limitation, data minimization, storage limitation, and confidentiality. This last one is most likely mentioned in most purchase orders, but it is hard to live up to in an industry which squeezes out every last cent in a long supply chain. 

Spicier is the fact that translators don’t need to know any personal data to translate a text, like who made the payment and how much money was transferred, as in the sample below. Anonymized source texts would address purpose limitation and data minimization. The biggest offenders, however, are the industry’s workhorses: neural machine translation (NMT) and translation memory (TM). NMT trains and TM stores texts full of personal data without means of deleting it, even though it was unnecessary for them to store the protected data in the first place. 

A GDPR-compliant translation workflow 

Some might argue that this difficult problem cannot be fixed. Well, it can. And not only this, our anonymization workflow saves money and increases quality and process safety, too. 

On a secure server ‘named entities’ (i.e. likely protected data) are recognized. This step is called NER, a standard discipline of Natural Language Processing. There are several anonymizers on the market, mainly supporting structured data and English, but they only support a one-way process. 

In our solution, the data is actually “pseudonymized” in both the source and target languages. This keeps the anonymized data readable for linguists by replacing protected data with another string of the same type. Once translated, the text is de-anonymized by replacing the pseudonyms with the original data. This step is tricky since the data also needs to be localized, as in our example with the title and the decimal and thousands separators. The TMs used along the supply chain will only store the anonymized data. Likewise, NMT is not trained with any personal data. 

Know-how

We recently did a feasibility study to test this approach. Academia considers NER a solved problem, but in reality it’s only somewhat done for English. Luckily, language models can now be trained to work cross-language. Rule-based approaches, like regular expressions, add deterministic process safety. For our study we extended the current standard formats for translation, TMX and XLIFF, to support pseudonymization. De-anonymization is hard, but I had already previously developed its basics for the first versions of TRADOS. 

What remains is the trade-off between data protection and translatability. The more text is anonymized, the better the leverage – but the harder the text is to understand for humans, too. Getting that balance right will still require some testing, best practices, and good UI design. For example, project managers will want a finer granularity on named entities than normally provided by NER tools. Using a multilingual knowledge system like Coreon, they could specify that all entities of type Committee are to be pseudonymized, but not entities of type Treaty

Anonymization is mandatory 

As shown above, a GDPR-compliant translation workflow is possible, and is thus legally mandatory. This is, in fact, good news. Regulations are often perceived as making life harder for businesses, but GDPR has actually created a sector in which the EU is a world leader. Our workflow enables highly-regulated industries, such as Life Sciences or Finance, to safely outsource translation. Service providers won’t have to sweat over confidentiality breaches. The workflow will increase quality as named entities are processed by machines in a secure and consistent way and machine translation has fewer possibilities to make stupid mistakes. It will also save a lot of money, since translation memories will deliver a much higher leverage.

If you want to know more, please contact us.

The post Making Translation GDPR-Compliant appeared first on The Multilingual Knowledge Blog.

12 DEC 2018

Sunsetting CAT

For decades Computer Assisted Translation based on sentence translation memories has been the standard tool for going global. Although CAT had been originally designed with a mid-90s PC in mind and there have been proposals for changing the underlying data model, the basic architecture of CAT has been left unchanged. The dramatic advances in Neural Machine Translation (NMT) have now made the whole product category obsolete.

NMT Crossing the Rubicon

While selling translation memory I always said, machines will only be able to translate once they understand text; and if one day they would, MT will be a mere…

For decades Computer Assisted Translation based on sentence translation memories has been the standard tool for going global. Although CAT had been originally designed with a mid-90s PC in mind and there have been proposals for changing the underlying data model, the basic architecture of CAT has been left unchanged. The dramatic advances in Neural Machine Translation (NMT) have now made the whole product category obsolete.

NMT Crossing the Rubicon

While selling translation memory I always said, machines will only be able to translate once they understand text; and if one day they would, MT will be a mere footnote of a totally different revolution. Now it turns out that neural networks, stacked deeply enough, do understand us sufficiently to create a well formed translation. Over the last two years NMT has progressed dramatically. It has now achieved “human parity” for important language pairs and domains. That changes everything.

Industry Getting it Wrong

Most players in the $50b translation industry, service providers but also their customers, think that NMT is just another source for a translation proposal. In order to preserve their established way of delivery they pitch the concept of “augmented translation”. However, if the machine translation is as good (or bad) as human translation, who would you have revise it, another translator or a subject matter expert? 
Yes, the expert who knows what the text is about. The workflow is thus changing to automatic translation and expert revision. Translation becomes faster, cheaper, and better!

Different Actors, Different Tools

A revision UI will have to look very different to a CAT tool. The most dramatic change is that a revision UI has to be extremely simple. To support the current model of augmented translation, CAT tools have become very powerful. However, their complexity can only be handled by a highly demanded group of maybe a couple ten thousand of professional translators globally.

For the new workflow a product design is required, that can support dozens of millions of, mostly occasional, expert revisers. Also, the revisers need to be pointed to the sentences which need revision. This requires multilingual knowledge.

Disruption Powered by Coreon

Coreon can answer the two key questions for using NMT in a professional translation workflow: a) which parts of the translated text are not fit-for-purpose and b) why not? To do so the multilingual knowledge system classifies linguistic assets, human resources, QA, and projects in a unified system which is expandable, dynamic, and provides fallback paths. In the future linguists will engineer localization workflows such as Semiox and create multilingual knowledge in Coreon. "Doing words” is left to NMT.

The post Sunsetting CAT appeared first on The Multilingual Knowledge Blog.