Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases

Knowledge graphics are highlighted. Gartner has now officially declared since embedding knowledge graphics into the 2018 hype cycle for emerging technologies. I did not have to wait for Gartner, but if I mentioned that the year was the chart year, it was the opening in 2018. People who are active in this field have the chance of opportunities and threats.

Knowledge graph is real. They have been running for at least 20 years. The graph of knowledge was the original definition and incarnation, related to expression and reasoning of knowledge. Elements such as controlled vocabulary, taxonomy, schema, ontology are all part of this based on the basis of norms and practical Web semantics.

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So, what has changed? How are sports knowledge graphs of Airbnb, Amazon, Google, LinkedIn, Uber, Zalando becoming the main business? How did Amazon and Microsoft join the crowd of graphic database providers with the latest products? And how can you do it?

A graph of knowledge before they calm

The knowledge graph is cool and you can see everything. But are they exactly what? It might seem like a simple question, but how do you create a graph of knowledge? From taxonomies to ontologies – schemas and rules with essentially different complexities – this is the way people have done it for years.

RDF, the standard used to encode these schemas, has a graphical structure. Therefore, calling knowledge encoded on a graphical structure, "graph of knowledge" seems obvious. And the data modelers who do it are called knowledge engineers and ontologists.

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There are many applications in these knowledge graphs, from catalog elements, to integration and publication of data on the Web, complex inference, and so on. For the most important ones, you can check, Airbnb, Amazon, Diffbot, Google, LinkedIn, Uber, Zalando. that's why People seasoned with knowledge graphs with hype.

Therefore, the knowledge chart is currently in a hype cycle for emerging technologies. It is not bad for technology with more than 20 years of salvation. (Image: Gartner)

Like any data modeling …

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