Jesus Barrasa
Neo4j
Jesús is the Director of Telco and Media Solutions with Neo4j. He holds a Ph.D. in Computer Science ( AI / Knowledge Representation ) from the Technical University of Madrid.

His academic and profesional career has been permanently connected to Graphs. He did his Ph.D. research on relational-to-graph schema mappings with the Ontology Engineering Group between 2001 and 2007. After that, he moved to London to join Ontology.com and lead the design and construction of graph-based solutions for Tier 1 telcos around the world using the RDF stack. For the last 4 years he's moved to the LPG camp, leading Neo4j’s efforts in the Telco and Media industry.

Given his expertise on both sides of the graph spectrum (RDF and LPG), Jesús has been publicly speaking/blogging/coding about how the two approaches can coexist and complement each other.
 

The Knowledge and the Graph, a deconstruction exercise

 
The combination of knowledge + graph has operated the magic and all industries are turning their heads to the new buzzword. However, there were graphs before the KG and similarly, there was Knowledge Representation before the KG.
In my talk, I propose :

  1. An analysis of the two elements separately: Graphs (as in data models) and Knowledge (as in explicit semantics). This will lead to
  2. A compared analysis of the two main approaches to implementing KG: Property Graphs and RDF Graphs. And finally,
  3. I'll explore a number of ways in which the two approaches (LPG and RDF) can coexist and complement each other, like:* Import-Export of RDF data and metadata to-from Neo4j* Use of ontologies in Neo4j. As we know, ontologies serve two main purposes: One, being explicit definitions of shared vocabularies for interoperability and TWO, being actionable fragments of explicit knowledge that general purpose engines can use for inferencing/reasoning. I'll explain how Property Graphs can benefit from both by demonstrating how to map Graph DB data in Neo4j to public vocabularies like schema.org and how to run ontology-driven inferences in Neo4j.
 

Some of these have been considered exclusive of the Semantic Web/Linked Data community, but I'll show how Graph Databases like Neo4j can democratize their use by giving them a pragmatic angle.