In this instalment of the QuickGraph series, I’ll show how to map a graph stored in Neo4j to an ontology (or schema, or vocabulary…) using the neosemantics extension. Continue reading “QuickGraph#7 Creating a schema.org linked data endpoint on Neo4j”
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)”
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.
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)”
As far as I know, the only way to query Google’s Knowledge Graph currently is the search API. Let’s run a query on it, search for instance for Miles Davis’ album “Sketches of Spain”. Continue reading “The ‘hidden’ connections in Google’s Knowledge Graph”
For this example, I am going to use a sample movie dataset from the Cayley project. It’s a set of half a million triples about actors, directors and movies that can be downloaded here. Continue reading “QuickGraph#3 A step-by-step example of RDF to Property Graph transformation”
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”