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Bergwinkl","email":"bergi@axolotlfarm.org","url":"https://www.bergnet.org/people/bergi/card#me"},"license":"MIT","homepage":"https://github.com/rdf-ext/grapoi","keywords":["edge","graph","node","path","rdf","rdfjs","traverse"],"repository":{"type":"git","url":"git+https://github.com/rdf-ext/grapoi.git"},"description":"Universal RDF/JS graph traverser","maintainers":[{"name":"bergos","email":"bergi@bergnet.org"}],"readme":"# grapoi\n\n[![build status](https://img.shields.io/github/actions/workflow/status/rdf-ext/grapoi/test.yaml?branch=master)](https://github.com/rdf-ext/grapoi/actions/workflows/test.yaml)\n[![npm version](https://img.shields.io/npm/v/grapoi.svg)](https://www.npmjs.com/package/grapoi)\n\nGrapoi is a JavaScript graph traversal library inspired by [Gremlin](https://tinkerpop.apache.org/gremlin.html).\nIt allows querying [RDF/JS Datasets](https://rdf.js.org/dataset-spec/) readable and intuitive way.\nGrapoi makes processing RDF data in JavaScript fun again.\n\nThe main purpose of Grapoi is to traverse and filter through datasets with ease.\nMost methods will return new object instances that refer back to the result terms after being processed by their respective functions.\n\nAdditionally, 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.\n\n## Install\n\n```bash\nnpm install grapoi --save\n```\n\n## Usage\n\n### Query Data\n\nThis example shows how to query an RDF/JS Dataset based on the [housemd](https://www.npmjs.com/package/housemd) dataset.\nIt covers how to use the methods `.out()` and `.in()` to traverse outwards or inwards. \n`.trim()` can be used to remove the tail of the traversing history, which is not of interest.\nOnce this is done, only those quads that are relevant can be listed with the `.quads()` method. \nA unique list of terms can be created with `.distinct()`.\n\n```javascript\nimport grapoi from 'grapoi'\nimport housemd from 'housemd'\nimport rdf from 'rdf-ext'\n\nconst ns = {\n  house: rdf.namespace('https://housemd.rdf-ext.org/'),\n  rdf: rdf.namespace('http://www.w3.org/1999/02/22-rdf-syntax-ns#'),\n  schema: rdf.namespace('http://schema.org/'),\n  xsd: rdf.namespace('http://www.w3.org/2001/XMLSchema#')\n}\n\nconst dataset = rdf.dataset(housemd({ factory: rdf }))\n\nconst people = grapoi({ dataset, term: ns.house('person/') })\n\nconsole.log('people in the house dataset:')\n\nfor (const quad of people.out(ns.schema.hasPart).quads()) {\n  console.log(`\\t${quad.object.value}`)\n}\n\nconst house = grapoi({ dataset, factory: rdf, term: 'Gregory' }).in().trim()\n\nconsole.log('properties of the guy named Gregory:')\n\nfor (const quad of house.out().quads()) {\n  console.log(`\\t${quad.predicate.value}: ${quad.object.value}`)\n}\n\nconst address = house\n  .out(ns.schema.homeLocation)\n  .out(ns.schema.address)\n\nconsole.log('address of the guy named Gregory:')\n\nfor (const quad of address.trim().out().quads()) {\n  console.log(`\\t${quad.predicate.value}: ${quad.object.value}`)\n}\n\nconst nationalities = house\n  .out(ns.schema.knows)\n  .out(ns.schema.nationality)\n  .distinct()\n\nconsole.log('nationalities of all known people:')\n\nfor (const value of nationalities.values) {\n  console.log(`\\t${value}`)\n}\n```\n\n### Create Data\n\nThis example shows how to create data with Grapoi.\nThe starting point is the term given in the `grapoi` factory function.\nQuads are added `.addOut()` from subject to object direction.\nNested structures can be created in the callback, which is called with a `grapoi` instance referring to the new object.\n\n```javascript\nimport grapoi from 'grapoi'\nimport rdf from 'rdf-ext'\n\nconst ns = {\n  ex: rdf.namespace('https://example.org/'),\n  rdf: rdf.namespace('http://www.w3.org/1999/02/22-rdf-syntax-ns#'),\n  schema: rdf.namespace('http://schema.org/'),\n  xsd: rdf.namespace('http://www.w3.org/2001/XMLSchema#')\n}\n\nconst dataset = rdf.dataset()\n\nconst person = grapoi({ dataset, factory: rdf, term: ns.ex.address })\n\nperson.addOut(ns.schema.homeLocation, location => {\n  location.addOut(ns.schema.address, address => {\n    address.addOut(ns.schema.addressLocality, 'Plainsboro Township')\n    address.addOut(ns.schema.addressRegion, 'NJ')\n    address.addOut(ns.schema.postalCode, '08536')\n    address.addOut(ns.schema.streetAddress, '221B Baker Street')\n  })\n})\n\nfor (const quad of dataset) {\n  console.log(`${quad.subject.value} - ${quad.predicate.value} - ${quad.object.value}`)\n}\n```","readmeFilename":"README.md"}