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.
By adding a semantic layer to data lakes, what we get is Enterprise Knowledge Graphs. Even though there are a number of approaches and implementations to representing graphs, a set of standards under the Linked Data moniker combined with extensible, curated vocabularies for numerous domains offers a lightweight semantically enriched approach to enterprise data integration.
Linked Data has had a rough time finding its way to the enterprise, but a lot of water has flowed under the bridge connecting the 2009 PwC technology forecast to the 2015 Gartner Hype Cycle for Advanced Analytics and Data Science. Linked Data are shown as currently being in the Trough of Disillusionment, expected to reach the Plateau of Productivity in the next 5-10 years, which according to industry pundits is a good thing as it means we’re finally getting there.
Promising-sounding or not, enterprises need more than cool technology and hype cycles to move to adoption: they need solutions for managing their data and metadata. Data solutions are well-known, metadata solutions less so, but adopters like BBC, Credit Suisse, and Roche show the way.
Managing data vocabularies and mappings is crucial for instating Enterprise Knowledge Graphs, and the aptly named Semantic Web Company (SWC) presented its own solution in this space called PoolParty. PoolParty is a semantic middleware that helps organizations develop knowledge graphs based on Semantic Web standards.