Defining semantics is a matter of semantics, not less so in the Big Data space.

The Semantics conference is one of the biggest events for all things semantics. Key research and industry players gathered this week in Leipzig to showcase and discuss, and we were there to get that vibe.

Graphs are everywhere: we have social graphs and knowledge graphs and office graphs, and in the minds of most these have been associated with Facebook and Google and Microsoft. But the concept of Knowledge Graphs is broader and vendor-agnostic.

All graphs can be considered as knowledge graphs, insofar as they represent information by means of nodes and (directional) edges. Nodes represent entities and edges represent relationships between them, such as Customer-buys-Product. Another way of stating this is by using the Subject-Predicate-Object metaphor borrowed from natural language.

However, not all information is represented by means of graphs, for a number of reasons mostly having to do with complexity, cost, and performance. In the enterprise, the new imperative to deal with such issues is the data lake: a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.

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