techpost:

A Property Graph has a set of Vertices and a set of Edges.

Each Vertex has:

• A unique Identifier.
• A set of outgoing Edges.
• A set of incoming Edges.
• A collection of properties defined by a map from key to value.

Each Edge has:

• A unique Identifier.
• An outgoing tail vertex.
• An incoming head vertex.
• A label that denotes the type of relationship between its two vertices.
• A collection of properties defined by a map from key to value.

dagoneye:

a brevissimo andrà online un lavoro in corso su privacy e advertising, applicato attraverso tecnologie del Semantic web .) #metafora stuff

This is another signal for Semantic web being mainstream…

The notion of provenance is essential when integrating data from different sources or on the Web. Therefore, state-of-the-art RDF repositories store subject-predicate-object-context quadruples, where the context typically denotes the provenance of a given statement.

N-Quads format extends N-Triples with context. Each triple in an N-Quads document can have an optional context value.

(via: late2theparty:)

This is a VERY (very) well written introduction to RDF and it’s relevance to the NoSQL movement. The article stresses that RDF is, currently (as of April 2010), the only NoSQL solution that is standardised upon, which includes the ability to query it via an SQL-like query interface, such as SPARQL.

If you like to see a small N-Triples (an RDF export format) and SPARQL sample, see this stackoverflow post. Given [from stackoverflow]:

1 <- 2 -> 3
3 <- 4 -> 5

these are already subject predicate object form so just slap some URI notation on it, load it in the triple store and query at-will via SPARQL. Here it is in NT (N-Triples) format:

<http://mycompany.com#1> <http://mycompany.com#2> <http://mycompany.com#3> .
<http://mycompany.com#3> <http://mycompany.com#4> <http://mycompany.com#5> .

Now query for all nodes two hops from node 1:

SELECT ?node
WHERE {
    <http://mycompany.com#1> ?p1 ?o1 .
    ?o1 ?p2 ?node .
}

This would of course yield <http://mycompany.com#5>.

What the example query is doing is identifying data associated with the specified input RDF ‘node’, and then requesting an RDF node adjacent to this node within another RDF triple/axis (forgive my terminology).

A notable RDF ‘store’ mentioned in the [comments of] the post above is Bigdata store, which is hailed as scaling very well (it is free and open-source).

Java RDF access libraries include Jena and Sesame, which intern should be accessible via JVM-compatible languages such as Scala.

Is this true?

dagoneye:

pero’: considerazioni per niente malaccio