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It is a quite established method in the US to structure enterprise data with taxonomies. American executives understand this is the only way to tame the data flood and to manage information. No surprise that Gartner has put enterprise taxonomies on the radar screen of key technologies. Strangely enough European companies still make little use of this powerful tool. The reason is the added complexity of the language dimension. But not anymore. Multilingual Knowledge Systems bring the power of taxonomies also to the European enterprise.

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Gartner: Taxonomy in Hype Cycle

Most IT folks are familiar with Gartner’s hype cycle. It places key emerging technologies on a curve which fatefully goes from hype, to disillusion, and then via enlightenment to productivity. Being put on the Gartner’s technology radar screen triggers a lot of attention of CIOs. This is great for terminologists, knowledge experts, and software vendors.
However, Gartner has put enterprise taxonomy in a difficult place on the hype curve. Also, Gartner predicts that it will still take some years till mainstream adoption. With other words you and we are pioneers and are working on the forefront of enterprise IT. While we see broader adoption already happening in the US and believe it will better happen soon in Europe, too, Gartner’s prediction is also good news. It allows innovative software companies to grow their products and scale with the market. And it allows you to introduce cutting-edge technology in your business and take full advantage of efficiency and productivity gains.

Gartner strategically adds enterprise taxonomy

Fascinating Learnings on AI and Knowledge Processing from LT Accelerate

This year’s LTA conference on multilingual Text Analytics showed how largely the field overlaps with AI and knowledge processing. Sentiment Analytics has passed the coarse good/bad/neutral segmentation and maps now to wealth of human emotions. The results correlate strongly with the much more expensive customer surveys. Needless to say products have to support multilingual since customers express emotions in their own language.
Bots are the new Apps. Especially younger customers rather want to chat than read manuals or search in tech blogs. Bots have to understand language, access enterprise knowledge, and be polyglot.
Machine Learning, deep and neural, is the approach of choice. Neural Machine translation is outperforming statistical MT. Machine Learning solves many mapping and categorization tasks. However, for serious matters, such as health, legal, or money Machine Learning needs to be based on human knowledge: on taxonomies, ontologies, and terms.

Symbiosis of Language Technology and AI at LT-Accelerate

Liebherr Deploys Coreon

Liebherr Rostock, one of the leading global manufacturers of maritime cranes, deploys Coreon as a Multilingual Knowledge System. After an in-depth market research and pilot phase Liebherr concluded that Coreon's solution fits best:

  1. Full descriptive power and a flexible data model
  2. Efficiency through multilingual maps: one concept system applicable to all languages
  3. Based on proven principles and standards
With the Coreon repository growing and growing, Liebherr now works on ...
  • incorporating more and more products
  • signing in more users outside of authoring and translation
  • deploying integrations with CAT and authoring tools
  • supplying of valuable multilingual knowledge to other business processes
  • linking multiple databases to develop a knowledge system joining diverse company data

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Introducing "Aliases" - to Tackle Important Needs in Manufacturing

In manufacturing processes components are re-used as often as possible. Whether from a library or a construction kit: one component – i.e. a concept in the knowledge system – becomes part of several modules and is assembled into several products. For instance, a bicycle wheel is part of the front fork as well as the rear frame. This means, in an MKS the concept 'wheel' must appear two times: as a child to 'front fork' and as a child to 'rear frame'.
The obvious solution would be polyhierarchies. Coreon supports multiple parent relationships, but it doesn’t yet solve the manufacturers’ challenge. Here is why:
Depending under which parent a concept is hooked, it can have different child concepts. For instance, the wheel of the front fork has additionally the component (the narrower concept) 'dynamo', whereas the wheel of the rear frame is equipped with the 'rear brake'. Polyhierarchies cannot model this.
Coreon's MKS resolves this by its unique "Alias" feature. A functionality that makes one and the same concept ('wheel') show up several times in the concept system. Intuitive and powerful, aliases work like shortcuts or placeholders.
With this unique capability manufacturers benefit from an efficient and effective way to model products, modules, and components via a Multilingual Knowledge System.

Aliases - to tackle important needs in manufacturing