Neo4j is your RDF store (part 3) : Thomson Reuters’ OpenPermID

If you’re new to RDF/LPG, here is a good introduction to the differences between both types of graphs.  
For the last post in this series, I will work with a larger public RDF dataset in Neo4j. We’ve already seen a few times that importing an RDF dataset into Neo4j is easy, so what I will focus on in this post is what I think is the more interesting part, which is what comes after the data import, here are some highlights:

  1. Applying transformations to the imported RDF graph to make it benefit from the LPG modelling capabilities and enriching the graph with additional complementary data sources.
  2. Querying the graph to do complex path analysis and use graph patterns to detect data quality issues like data duplication and also to profile your dataset
  3. Integrate Neo4j with standard BI tools to build nice charts on the output of Cypher queries on your graph.
  4. Building an RDF API on top of your Neo4j graph.

All the code I’ll use is available on GitHub. Enjoy!

Continue reading “Neo4j is your RDF store (part 3) : Thomson Reuters’ OpenPermID”

Neo4j is your RDF store (part 2)

As in previous posts, for those of you less familiar with the differences and similarities between RDF and the Property Graph, I recommend you watch this talk I gave at Graph Connect San Francisco in October 2016.

In the previous post on this series, I showed the most basic way in which a portion of your graph can be exposed as RDF. That was identifying a node by ID or URI if your data was imported from an RDF dataset. In this one, I’ll explore a more interesting way by running Cypher queries and serialising the resulting subgraph as RDF. Continue reading “Neo4j is your RDF store (part 2)”

Neo4j is your RDF store (part 1)

If you want to understand the differences and similarities between RDF and the Labeled Property Graph implemented by Neo4j, I’d recommend you watch this talk I gave at Graph Connect San Francisco in October 2016.

Intro

Let me start with some basics: RDF is a standard for data exchange, but it does not impose any particular way of storing data.

What do I mean by that? I mean that data can be persisted in many ways: tables, documents, key-value pairs, property graphs, triple graphs… and still be published/exchanged as RDF. Continue reading “Neo4j is your RDF store (part 1)”

Building a semantic graph in Neo4j

There are two key characteristics of RDF stores (aka triple stores): the first and by far the most relevant is that they represent, store and query data as a graph. The second is that they are semantic, which is a rather pompous way of saying that they can store not only data but also explicit descriptions of the meaning of that data. The RDF and linked data community often refer to these explicit descriptions as ontologies. In case you’re not familiar with the concept, an ontology is a machine-readable description of a domain that typically includes a vocabulary of terms and some specification of how these terms inter-relate, imposing a structure on the data for such domain. This is also known as a schema. In this post, both terms schema and ontology will be used interchangeably to refer to these explicitly described semantics. Continue reading “Building a semantic graph in Neo4j”