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Dschingis Khan & Knowledge Graphs

With Knowledge Graphs, data from heterogeneous sources can be connected and visualised. The resulting semantic networks help to uncover connections and gain new information. An introduction with current examples.

By Ivana Leiseder

If you enter the search term "Dschingis Khan" on Google, you get an overview of the most important content on the first page of results, clearly arranged and visualised. For example, in a box you will find the key data on the life of the Emperor of the Mongol Empire and a list of terms that are also often searched for contextually, including a hint that "Dschingis Khan" is also a pop band. (In case we had forgotten, we now remember the 70s medley at the latest ... "Hu, ha, Dsching-Dsching-Dschingis Khan ...") To the left of this you will find the most important videos and further down the most frequently asked questions including answers about the search term. In just a few seconds you know everything you need to know. Clever, isn't it?

Source: Screenshot Google

Do the same experiment with the search term "Corona" ... It's amazing what Google can provide us with in terms of information and graphics in a fraction of a second.

These two examples show that Google is increasingly outpacing encyclopaedias like Wikipedia. But how does the search engine manage to show us contextually relevant content in the first place? This is made possible by Knowledge Graphs, semantic knowledge databases that store heterogeneous data from different sources, link them and make them searchable. Depending on requirements, a semantic network created in this way can also be visualised, as is the case on Google with the search term "Corona".

However, Knowledge Graphs are not only relevant for search engines, but are also already being used in research and tourism. More and more companies are also recognising the benefits of Knowledge Graphs, for example in risk management. It is precisely in this area that we have developed a Knowledge Graph for a banking association, which automatically recognises dependencies between groups and associated companies and presents them in a comprehensible and transparent way. For this purpose, we harmonised all available company data across a wide range of internal and external data sources and integrated them into the Knowledge Graph. With the solution, the banking association can reduce credit risk and be sure to only grant sufficiently secured loans.


Source: Trivadis


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