Use enterprise knowledge – globally across boundaries

Coreon combines taxonomies with terminology
to create and deploy Multilingual Knowledge Systems.
Coreon makes search, machine learning, and
IoT applications interoperable.

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Enable

Build your Information Infrastructure

Multilingual knowledge systems link knowledge (meaning) with language (words).

Flat vocabularies are enriched with semantic structures. Thesauri, taxonomies are enhanced with more languages.

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Leverage

Visually Explore Semantic Assets

With Coreon organizations build and explore taxonomies, ontologies, and terminology within one single system, in one view.

Linguistic quality tools keep the data clean and consistent for reliable reuse in various applications.

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Benefit

Discover Knowledge in Data

Half of big data is unstructured – Coreon turns textual, multilingual information into actionable knowledge.

Benefit from intelligent big data mining. Facilitate cross-border interoperability. Master language.

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News

18 SEP 2017

Multilingual Knowledge supporting AI, IoT, and Industry 4.0

A Review on Summer Events

We would like to share some impressions from recent events and conferences. The interesting common denominator was the following themes: how can we leverage and deploy terminology assets in other business processes? How can we deploy the valuable knowledge in terminology assets to support AI, Machine Learning, Internet of Things, and Industry 4.0?


Coreon Innovation Seminar 

The Future of Human Expert Knowledge

Experts in machine learning and industry consultants gathered in Berlin to discuss and brainstorm about the opportunities Coreon provides for the diverse fields they work in. The Coreon use cases presented were…

A Review on Summer Events

We would like to share some impressions from recent events and conferences. The interesting common denominator was the following themes: how can we leverage and deploy terminology assets in other business processes? How can we deploy the valuable knowledge in terminology assets to support AI, Machine Learning, Internet of Things, and Industry 4.0?


Coreon Innovation Seminar 

The Future of Human Expert Knowledge

Experts in machine learning and industry consultants gathered in Berlin to discuss and brainstorm about the opportunities Coreon provides for the diverse fields they work in. The Coreon use cases presented were: Cross-border e-commerce, AI expert know-how for knowledge heavy applications, and EU Institutions and interoperability. The event was by personal invitation only and was a huge success. We look forward to repeating it soon! Please click here if you would like to be invited next time.

ILKR 2017: Industry 4.0 meets Language and Knowledge Resources

The first trip brought us to Vienna to the Austrian Standards Institute. The ILKR 2017 took place just ahead of the ISO TC37 annual meeting. As its title suggests, ILKR tackles the question how multilingual knowledge resources enable Industry 4.0. Thus many presentations explored the possibilities around multilingual knowledge management, knowledge transfer, and new business models.

No Industry 4.0 without Semantics

Our contribution illustrated why the Internet of Things and Industry 4.0 need semantics. When hardware devices speak to each other, they interoperate. This requires a mutual understanding of what they actually do, like “I measure temperature.
Interoperability by Multilingual Knowlegde System MKS semantic mapping
What do you measure?” The answer is in the semantic of the devices’ metadata. We explained how Multilingual Knowledge Systems (MKS) resolve this challenge and how they facilitate interoperability. And how existing terminologies, taxonomies, and ontologies can be re-purposed to become an MKS.

ILKR was followed by a pretty exciting workshop on eCl@ss and Multilingual Product Master Data Management. It had a particular focus on how e-procurement processes benefit from classifications and knowledge systems.

 

TSS 2017: Terminology Summer School

This year back in Cologne, the TSS is a five day course that attracts participants worldwide who look for a kick-start in terminology and knowledge resource management. During the first 3 days, TSS usually hovers around the fundamentals of terminology management and its role in business processes. Then we were invited to give two presentations:
    Michael Wetzel, Coreon MD, about KOS and Semantic Web
  1. Terminologies and other Knowledge Organization Systems (KOS): What is a KOS, what are its benefits, typical examples, the role it plays in the Semantic Web? What is the difference between a classification, a taxonomy, a thesaurus, and an ontology?
  2. Knowledge meets Language: Multilingual Concept Maps: How Coreon is a fusion of terminology with taxonomy / ontology, what benefits organizations enjoy by deploying Multilingual Knowledge Systems
Coreon is proud to be a regular sponsor of TSS, and we look forward to next year, then again in Vienna (9-13 July 2018).

 

Terminology - Ontology Round Table

Mid-August we were invited to a one day workshop on touch-points between terminology and ontology data and science. It took place at the HS Karlsruhe, sponsored by DIT, and organized by Petra Drewer, Francois Massion, and Donatella Pulitano. The workshop benefited from a valuable mix of participants: academic researchers from the terminology and ontology world, industry and institutional representatives (SAP, DIN, Deutsche Bahn …), and tool vendors. Its goal was to find commonalities and differences between the two disciplines. As a provider of a unified solution we contributed to the workshop by illustrating how Coreon customers benefit from a fusion of terminology with ontology. Experts confirmed our claim that humanly curated resources, i.e. MKS, are indispensable to make Machine Learning work for less resourced domains and languages.

We recommend Petra’s and Francois’ presentation at the upcoming tekom conference on exactly that topic, Wed, 25 Oct, 11:15: Why Artificial Intelligence requires intelligent terminologies (and terminologists)!

See Coreon live this Autumn 2017

And of course, we’d be happy to meet you on upcoming events this autumn:
  • LT Industry Summit, 9-11 Oct, Brussels
    Meet Jochen Hummel, Coreon CEO and Chairman of the Board of LT Innovate at the event. Do not miss the opening keynote by Marija Gabriel, Commissioner for Digital Economy and Society, and Jochen's panel session "Artificial Intelligence: Hype or Reality?" on Oct 10, 9am.
  • tekom / tcworld, 24-26 Oct, Stuttgart
    Find us in the large hall C2, booth 2/G04 together with our partner company Semantix.
    We are proud to present recent highlights, such as brand new filtering capabilities and inline formatting! Learn how Multilingual Knowledge Systems boost AI and Machine Learning solutions and how they make the Internet of Things and Industry 4.0 work. Join us for a product demo Tuesday afternoon, 14:45 room C10.1.
Happy networking!
6 JUN 2017

The IoT will Thrive on Semantics

Why the IoT will thrive on Semantics?In the Internet of Things (IoT) all devices are supposed to communicate among themselves, worldwide. Only, what are they saying to each other? Recently, former Siemens CTO Siegfried Russwurm got to the core of the issue: “Industry 4.0 needs first of all semantics. We can only get through interfaces and breaking points using unified semantics." Apparently not only civil servants in cross-border projects or industry supply chain managers need semantic interoperability. The billions and billions of IoT devices need semantic interoperability as well.

The Must of Semantic Technologies


Sebastian Tramp, coordinator of the Linked Enterprise Data Services…
Why the IoT will thrive on Semantics?In the Internet of Things (IoT) all devices are supposed to communicate among themselves, worldwide. Only, what are they saying to each other? Recently, former Siemens CTO Siegfried Russwurm got to the core of the issue: “Industry 4.0 needs first of all semantics. We can only get through interfaces and breaking points using unified semantics." Apparently not only civil servants in cross-border projects or industry supply chain managers need semantic interoperability. The billions and billions of IoT devices need semantic interoperability as well.

The Must of Semantic Technologies


Sebastian Tramp, coordinator of the Linked Enterprise Data Services (LEDS) project, nicely explains why the vision of the IoT and Industry 4.0 cannot be realized without semantics. If the meaning of IoT devices is not clear, it’s hard for them to interact or even communicate. For this the devices and their relevant metadata must be clearly defined. If, for example, some value is supposed to be measured, the data stream needs to contain information which sensor took the value when and where. But also what this value is all about. The power of the IoT is based on combining data from different sources. To link this data in a meaningful way you need interfaces in form of shared knowledge, i.e. ontologies. That’s what semantic technologies deliver.

Textual Metadata


Human language plays a surprisingly big role in the IoT. For example, a visual sensor’s image Exif information records under [Flash mode] the value “flash, red eye, no strobe return”. Another device processing this textual metadata needs to understand what “… red eye, no strobe …” actually means. And, very important, if it can’t provide specific processing for the strobe usage, it should conclude the more generic fact that a flash was active. To make things even more complex, depending on where the device was built it might say this in Chinese or German.

Leverage Terminologies, Taxonomies, and Ontologies


Luckily Multilingual Knowledge Systems (MKS) like Coreon deliver the required semantic and linguistic intelligence for the communication of IoT devices. Companies can leverage existing resources such as word lists, multilingual termbases, and taxonomies to build their metadata concepts with corresponding labels in one or more languages. The metadata concepts need to be semantically structured at least in broader-narrower relations. Through auto-taxonomisation a provisional graph is suggested which is reviewed and finalised by subject matter experts. Knowledge resources require often coverage of several languages. Mono- and bilingual term extraction, text and translation memory harvesting algorithms reduce this effort significantly.

This way a knowledge graph is created with each node representing a metadata meaning expressed by one or more labels. When shared this graph become the interface for IoT devices.

Semantics for the IoT


Without semantic interoperability IoT devices fail to communicate with each other. If human intervention is necessary the Internet of Things with billions of devices remains a buzzword for a great vision. Multilingual Knowledge Systems are a proven solution to make data repositories, systems, organizations, and even countries interoperable. They will provide the unified semantics for the Internet of Things, globally.

Learn more about Coreon or jump right in for a look and feel.
5 APR 2017

Why Machine Learning still Needs Humans for Language?

Outperforming Humans

Machine Learning (ML) begins to outperform humans in many tasks which seemingly require intelligence. The hype about ML makes it even into mass media ML can read lips, recognizes faces, or transform speech to text. But when ML has to deal with the ambiguity, variety and richness of language, when it has to understand text or extract knowledge, ML continues to need human experts.

Knowledge is Stored as Text

The Web is certainly our greatest knowledge source. However, the Web has been designed for being consumed by humans, not by machines. The Web’s knowledge is mostly stored in…

Outperforming Humans

Machine Learning (ML) begins to outperform humans in many tasks which seemingly require intelligence. The hype about ML makes it even into mass media. ML can read lips, recognizes faces, or transform speech to text. But when ML has to deal with the ambiguity, variety and richness of language, when it has to understand text or extract knowledge, ML continues to need human experts.

Knowledge is Stored as Text

The Web is certainly our greatest knowledge source. However, the Web has been designed for being consumed by humans, not by machines. The Web’s knowledge is mostly stored in text and spoken language, enriched with images and video. It is not a structured relational database storing numeric data in machine processable form.

Text is Multilingual

The Web is also very multilingual. Recent statistics show that surprisingly only 27% of the Web’s content is English and only 21% in the next 5 most used languages. That means more than half of its knowledge is expressed in a long tail of other languages.

Constraints of Machine Learning

 

ML faces some serious challenges. Even with today’s availability of hardware, the demand for computing power can become astronomical when input and desired output are rather fuzzy (see the great NYT article "The Great A.I. Awakening").

ML is great for 80/20 problems, but it is dangerous in contexts with high accuracy needs: “Digital assistants on personal smartphones can get away with mistakes, but for some business applications the tolerance for error is close to zero", emphasizes Nikita Ivanov, from Datalingvo, a Silicon Valley startup.

ML performs good on n-to-1 questions. For instance, in face recognition “all these pixel show which person?” has only one correct answer. However, ML is struggling in n-to-many or in gradual circumstances … there are many ways to translate a text correctly or express a certain piece of knowledge.

ML is only as good as its available relevant training material. For many tasks mountains of data are needed. And the data better be of supreme quality. For language related tasks these mountains of data are often required per language and per domain. Further, it is also hard to decide when the machine has learned enough.

Monolingual ML Good enough?

 

Some suggest why not process everything in English. ML does also an OK job at Machine Translation, like Google Translate. So why not translate everything into English and then lets run our ML algorithms? This is a very dangerous approach since errors multiply. If the output of an 80% accurate Machine Translation becomes the input to an 80% accurate Sentiment Analysis errors multiply to 64%. At that hit rate you are getting close to flipping a coin. 


 

Human Knowledge to Help

 

The world is innovating constantly. Every day new products and services are created. To talk about them we continuously craft new words: the bumpon, the ribbon, a plug-in hybrid, TTIP ‒ only with the innovative force of language we can communicate new things.

Struggle with Rare Words

By definition new words are rare. They first appear in one language and then may slowly propagate into other domains or languages. There is no knowledge without these rare words, the terms. Look at a typical product catalog description with the terms highlighted. Now imagine this description without the terms – it would be nothing but a meaningless scaffold of fill-words.


Knowledge Training Required

At university we acquire the specific language, the terminology, of the field we are studying. We become experts in that domain. But even so, later in our professional career when we change jobs we still have to acquire the lingo of the new company: names of products, modules, services, but also job roles and their titles, names for departments, processes, etc. We get familiar with a specific corporate language by attending training, by reading policies, specifications, and functional descriptions. Machines need to be trained in the very same way with that explicit knowledge and language.

Multilingual Knowledge Systems Boost ML with Knowledge

 

There is a remedy: Terminology databases, enterprise vocabularies, word lists, glossaries – organizations usually already own an inventory of “their” words. This invaluable data can be leveraged to boost ML with human knowledge: by transforming these inventories into a Multilingual Knowledge System (MKS). An MKS captures not only all words in all registers in all languages, but structures them into a knowledge graph (a 'convertible' IS-A 'car' IS-A 'vehicle'…, 'front fork' IS-PART of 'frame' IS-PART of 'bicycle').

It is the humanly curated Multilingual Knowledge System that enables ML and Artificial Intelligence solutions to work for specific domains with only small amounts of textual data and also for less resourced languages.

Meet us

22 - 24 MAR 2018 (9 am - 6 pm)

DTT Symposion 2018

Bi-annual conference of the German Terminologists Association ("DTT"). Focus topics 2018 are: "Terminology and Text", "Terminology and A.i." and "Terminology Tools". Further tutorials on "Term Creation and Selection", "Research and Definitions", "Terminology Circles", "Ontology: Foundations, Tools, Synergies". Coreon will exhibit at the event.

Dorint Kongresshotel Mannheim

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