@hishprorg/iure-fuga

Grapoi is a JavaScript graph traversal library inspired by Gremlin. It allows querying RDF/JS Datasets readable and intuitive way. Grapoi makes processing RDF data in JavaScript fun again.

The main purpose of Grapoi is to traverse and filter through datasets with ease. Most methods will return new object instances that refer back to the result terms after being processed by their respective functions.

Additionally, Grapoi keeps track of all quads traversed from the start term, making it possible for you to keep track of how each result was found during the query process if needed - thus providing extra flexibility when dealing with complex datasets where multiple paths might lead towards desired results.

Install

1npm install @hishprorg/iure-fuga --save

Usage

Query Data

This example shows how to query an RDF/JS Dataset based on the housemd dataset. It covers how to use the methods .out() and .in() to traverse outwards or inwards. .trim() can be used to remove the tail of the traversing history, which is not of interest. Once this is done, only those quads that are relevant can be listed with the .quads() method. A unique list of terms can be created with .distinct().

1import @hishprorg/iure-fuga from '@hishprorg/iure-fuga'
2import housemd from 'housemd'
3import rdf from 'rdf-ext'
4
5const ns = {
6  house: rdf.namespace('https://housemd.rdf-ext.org/'),
7  rdf: rdf.namespace('http://www.w3.org/1999/02/22-rdf-syntax-ns#'),
8  schema: rdf.namespace('http://schema.org/'),
9  xsd: rdf.namespace('http://www.w3.org/2001/XMLSchema#')
10}
11
12const dataset = rdf.dataset(housemd({ factory: rdf }))
13
14const people = @hishprorg/iure-fuga({ dataset, term: ns.house('person/') })
15
16console.log('people in the house dataset:')
17
18for (const quad of people.out(ns.schema.hasPart).quads()) {
19  console.log(`\t${quad.object.value}`)
20}
21
22const house = @hishprorg/iure-fuga({ dataset, factory: rdf, term: 'Gregory' }).in().trim()
23
24console.log('properties of the guy named Gregory:')
25
26for (const quad of house.out().quads()) {
27  console.log(`\t${quad.predicate.value}: ${quad.object.value}`)
28}
29
30const address = house
31  .out(ns.schema.homeLocation)
32  .out(ns.schema.address)
33
34console.log('address of the guy named Gregory:')
35
36for (const quad of address.trim().out().quads()) {
37  console.log(`\t${quad.predicate.value}: ${quad.object.value}`)
38}
39
40const nationalities = house
41  .out(ns.schema.knows)
42  .out(ns.schema.nationality)
43  .distinct()
44
45console.log('nationalities of all known people:')
46
47for (const value of nationalities.values) {
48  console.log(`\t${value}`)
49}

Create Data

This example shows how to create data with Grapoi. The starting point is the term given in the @hishprorg/iure-fuga factory function. Quads are added .addOut() from subject to object direction. Nested structures can be created in the callback, which is called with a @hishprorg/iure-fuga instance referring to the new object.

1import @hishprorg/iure-fuga from '@hishprorg/iure-fuga'
2import rdf from 'rdf-ext'
3
4const ns = {
5  ex: rdf.namespace('https://example.org/'),
6  rdf: rdf.namespace('http://www.w3.org/1999/02/22-rdf-syntax-ns#'),
7  schema: rdf.namespace('http://schema.org/'),
8  xsd: rdf.namespace('http://www.w3.org/2001/XMLSchema#')
9}
10
11const dataset = rdf.dataset()
12
13const person = @hishprorg/iure-fuga({ dataset, factory: rdf, term: ns.ex.address })
14
15person.addOut(ns.schema.homeLocation, location => {
16  location.addOut(ns.schema.address, address => {
17    address.addOut(ns.schema.addressLocality, 'Plainsboro Township')
18    address.addOut(ns.schema.addressRegion, 'NJ')
19    address.addOut(ns.schema.postalCode, '08536')
20    address.addOut(ns.schema.streetAddress, '221B Baker Street')
21  })
22})
23
24for (const quad of dataset) {
25  console.log(`${quad.subject.value} - ${quad.predicate.value} - ${quad.object.value}`)
26}