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

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.
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:

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!

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.

Outperforming Humans

Knowledge is Stored as Text

Text is Multilingual

Constraints of Machine Learning

 

Monolingual ML Good enough?

 

 

Human Knowledge to Help

 

Struggle with Rare Words

Knowledge Training Required

Multilingual Knowledge Systems Boost ML with Knowledge