examples/ArangoRDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VBMhu_S_A2-3"
   },
   "source": [
    "# **ArangoRDF**\n",
    "\n",
    "<img src=\"https://github.com/ArangoDB-Community/ArangoRDF/blob/main/examples/assets/adb_logo.png?raw=1\" alt=\"rdf\" width=\"60\"/>\n",
    "<img src=\"https://github.com/ArangoDB-Community/ArangoRDF/blob/main/examples/assets/rdf_logo.png?raw=1\" alt=\"rdf\" width=\"50\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "44mc2EvIAzDy"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W4WlZsOCAjgV"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install arango-datasets\n",
    "!pip install adb-cloud-connector\n",
    "!pip install arango-rdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wMtfVlvuApDp"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# ArangoDB\n",
    "from arango import ArangoClient\n",
    "from arango_rdf import ArangoRDF\n",
    "from arango_datasets import Datasets\n",
    "from adb_cloud_connector import get_temp_credentials\n",
    "\n",
    "# RDF\n",
    "from rdflib import Graph, ConjunctiveGraph, URIRef, Literal, Namespace\n",
    "from rdflib.namespace import RDFS, XSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9L0Q4cGTMGMG"
   },
   "outputs": [],
   "source": [
    "def get_graph(data: str, is_conjunctive_graph: bool = False) -> Graph:\n",
    "  g = ConjunctiveGraph() if is_conjunctive_graph else Graph()\n",
    "  g.parse(data=data, format='trig' if is_conjunctive_graph else 'ttl')\n",
    "  return g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yRuJ3OIGE2Yr"
   },
   "source": [
    "# Understanding RDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z1SFNei_imUf"
   },
   "source": [
    "RDF is a standard model for data interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ, and it specifically supports the evolution of schemas over time without requiring all the data consumers to be changed.\n",
    "\n",
    "RDF extends the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link (this is usually referred to as a \"triple\"). Using this simple model, it allows structured and semi-structured data to be mixed, exposed, and shared across different applications.\n",
    "\n",
    "This linking structure forms a directed, labeled graph, where the edges represent the named link between two resources, represented by the graph nodes. This graph view is the easiest possible mental model for RDF and is often used in easy-to-understand visual explanations.\n",
    "\n",
    "Resources to get started:\n",
    "\n",
    "* [RDF Primer](https://www.w3.org/TR/rdf11-concepts/)\n",
    "* [RDFLib (Python)](https://pypi.org/project/rdflib/)\n",
    "* [One Example for Modeling RDF as ArangoDB Graphs](https://www.arangodb.com/docs/stable/data-modeling-graphs-from-rdf.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4VzvpJ2EuuMJ"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ssq_ohNZu0q_"
   },
   "source": [
    "[RDFLib](https://github.com/RDFLib/rdflib) is a pure Python package for working with RDF.\n",
    "\n",
    "RDFLib aims to be a pythonic RDF API. RDFLib's main data object is a `Graph` which is a Python collection of RDF Subject, Predicate, Object Triples:\n",
    "\n",
    "To create graph and load it with RDF data from DBPedia then print the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xiRxsYqY52BJ",
    "outputId": "adcd539c-dcf5-4e89-ab50-091406af9832"
   },
   "outputs": [],
   "source": [
    "g = Graph()\n",
    "g.parse('http://dbpedia.org/resource/Semantic_Web')\n",
    "\n",
    "for s, p, o in g:\n",
    "    print(s, p, o)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tibPlFLZ56cZ"
   },
   "source": [
    "The components of the triples are URIs (resources) or Literals (values).\n",
    "\n",
    "URIs are grouped together by namespace, common namespaces are included in RDFLib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vhpFtnqF57x9"
   },
   "outputs": [],
   "source": [
    "from rdflib.namespace import DC, DCTERMS, DOAP, FOAF, SKOS, OWL, RDF, RDFS, VOID, XMLNS, XSD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f1tb4nb15_Gl"
   },
   "source": [
    "You can use them like this:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "66dQYVV46BLH"
   },
   "outputs": [],
   "source": [
    "g = Graph()\n",
    "semweb = URIRef('http://dbpedia.org/resource/Semantic_Web')\n",
    "val = g.value(semweb, RDFS.label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1dz2_jcA6Cls"
   },
   "source": [
    "Where `RDFS` is the RDFS namespace, `XSD` the XML Schema Datatypes namespace and `g.value` returns an object of the triple-pattern given (or an arbitrary one if multiple exist).\n",
    "\n",
    "Or like this, adding a triple to a graph `g`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G-zlPMs36LF3",
    "outputId": "db3d3e78-fd84-4f55-e56d-be28dd990191"
   },
   "outputs": [],
   "source": [
    "g.add((\n",
    "    URIRef(\"http://example.com/person/nick\"),\n",
    "    FOAF.givenName,\n",
    "    Literal(\"Nick\", datatype=XSD.string)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4UvegpOD6QBC"
   },
   "source": [
    "The triple (in n-triples notation) `<http://example.com/person/nick> <http://xmlns.com/foaf/0.1/givenName> \"Nick\"^^<http://www.w3.org/2001/XMLSchema#string> .` is created where the property `FOAF.givenName` is the URI `<http://xmlns.com/foaf/0.1/givenName>` and `XSD.string` is the URI `<http://www.w3.org/2001/XMLSchema#string>`.\n",
    "\n",
    "You can bind namespaces to prefixes to shorten the URIs for RDF/XML, Turtle, N3, TriG, TriX & JSON-LD serializations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XXVr_m726aGX"
   },
   "outputs": [],
   "source": [
    "g.bind(\"foaf\", FOAF)\n",
    "g.bind(\"xsd\", XSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ag8fFkN6cDK"
   },
   "source": [
    "This will allow the n-triples triple above to be serialised like this:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VBiHxQ8I6ckC",
    "outputId": "4bad19d3-f29a-4740-c0d3-e1278f9156a6"
   },
   "outputs": [],
   "source": [
    "print(g.serialize(format=\"turtle\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fU3ZW4Up6kft"
   },
   "source": [
    "New Namespaces can also be defined:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "g9y5vAR96m5J"
   },
   "outputs": [],
   "source": [
    "dbpedia = Namespace('http://dbpedia.org/ontology/')\n",
    "\n",
    "abstracts = list(x for x in g.objects(semweb, dbpedia['abstract']) if x.language=='en')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KnQifktFAxHx"
   },
   "source": [
    "# Setup ArangoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ETS8l_NSAv0F",
    "outputId": "e4242d73-6443-4292-9977-c3e9670d17ee"
   },
   "outputs": [],
   "source": [
    "# Request temporary instance from the managed ArangoDB Cloud Service.\n",
    "con = get_temp_credentials()\n",
    "print(json.dumps(con, indent=2))\n",
    "\n",
    "# Connect to the db via the python-arango driver\n",
    "db = ArangoClient(hosts=con[\"url\"]).db(\n",
    "    con[\"dbName\"],\n",
    "    con[\"username\"],\n",
    "    con[\"password\"],\n",
    "    verify=True\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QfE_tKxneG9A"
   },
   "source": [
    "# Instantiate ArangoRDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kGfhzPT9eG9A"
   },
   "source": [
    "Connect ArangoRDF to our temporary ArangoDB instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oG496kBeeG9A",
    "outputId": "ca895acc-a9ad-4287-8364-7591618b8b5b"
   },
   "outputs": [],
   "source": [
    "adbrdf = ArangoRDF(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znQCjOwt7zBz"
   },
   "source": [
    "# <u>RDF to ArangoDB</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0qry3Bcy-160"
   },
   "source": [
    "#### ❗ RPT vs PGT ❗\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ONWNS6t8x7A"
   },
   "source": [
    "ArangoRDF's RDF-to-ArangoDB functionality has been implemented using concepts described in the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781).\n",
    "\n",
    "**TL;DR**:\n",
    "\n",
    "ArangoRDF has 2 RDF-to-ArangoDB transformation methods:\n",
    "\n",
    "1.   **RDF-topology Preserving Transformation** (RPT): `ArangoRDF.rdf_to_arangodb_by_rpt()`\n",
    "\n",
    "2.   **Property Graph Transformation** (PGT): `ArangoRDF.rdf_to_arangodb_by_pgt()`\n",
    "\n",
    "RPT preserves the RDF Graph structure by transforming each RDF Statement into an ArangoDB Edge.\n",
    "\n",
    "PGT ensures that Datatype Property Statements (i.e Literal Statements) are mapped as ArangoDB Document Properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U_sBs3jc96e3"
   },
   "source": [
    "```ttl\n",
    "@prefix ex: <http://example.org/> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "ex:book ex:publish_date \"1963-03-22\"^^xsd:date .\n",
    "ex:book ex:pages \"100\"^^xsd:integer .\n",
    "ex:book ex:cover 20 .\n",
    "ex:book ex:index 55 .\n",
    "```\n",
    "\n",
    "| RPT | PGT |\n",
    "|:-------------------------:|:-------------------------:|\n",
    "| ![image](https://user-images.githubusercontent.com/43019056/232347662-ab48ebfb-e215-4aff-af28-a5915414a8fd.png) | ![image](https://user-images.githubusercontent.com/43019056/232347681-c899ef09-53c7-44de-861e-6a98d448b473.png) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRutdKii-Pk5"
   },
   "source": [
    "#### Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2PWrF6C5VByv"
   },
   "source": [
    "RDF Graph Cases taken from the paper [*Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches*](https://arxiv.org/abs/2210.05781)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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   "outputs": [],
   "source": [
    "# Case 1: Standard RDF statement\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:alice a ex:Person .\n",
    "ex:bob a ex:Person .\n",
    "ex:alice ex:meets ex:bob .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
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   "source": [
    "# Case 2: The predicate of an RDF statement is subject in another statement\n",
    "# Case 2.1: Predicate as subject and literal as object\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
    "\n",
    "ex:Sam ex:mentor ex:Lee .\n",
    "ex:mentor rdfs:label \"project supervisor\" .\n",
    "ex:mentor ex:name \"mentor's name\" .\n",
    "\n",
    "ex:Sam a ex:Person .\n",
    "ex:Lee a ex:Person .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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   "source": [
    "# Case 2: The predicate of an RDF statement is subject in another statement\n",
    "# Case 2.2: Predicate as subject and RDF resource as object\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:Martin ex:mentorJoe ex:Joe.\n",
    "ex:mentorJoe ex:alias ex:teacher .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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   "source": [
    "# Case 2: The predicate of an RDF statement is subject in another statement\n",
    "# Case 2.3: Predicate as subject and RDF property as object - rdfs:subPropertyOf\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:Jan a ex:Person .\n",
    "ex:Leo a ex:Person .\n",
    "ex:Jan ex:supervise ex:Leo .\n",
    "\n",
    "ex:supervise rdfs:subPropertyOf ex:administer .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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   "source": [
    "# Case 2: The predicate of an RDF statement is subject in another statement\n",
    "# Case 2.4: Predicate as subject and RDF class as object - rdf:type\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.\n",
    "\n",
    "ex:Tom ex:friend ex:Chris .\n",
    "ex:friend rdf:type ex:relation .\n",
    "\"\"\"\n",
    "\n",
    "g = Graph()\n",
    "g.parse(data=data)\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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   "source": [
    "# Case 3: Data types and language tags\n",
    "# Case 3.1: Datatype property statements with different data types of the literal objects\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "ex:book ex:publish_date \"1963-03-22\" .\n",
    "ex:book ex:pages \"100\"^^xsd:integer .\n",
    "ex:book ex:cover 20 .\n",
    "ex:book ex:index \"55\" .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
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   "outputs": [],
   "source": [
    "# Case 3: Data types and language tags\n",
    "# Case 3.2: Datatype property statements with different language tags of the literal objects\n",
    "# NOTE: PGT will currently discard the language tags!\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:book ex:title \"Book\"@en.\n",
    "ex:book ex:title \"Bog\"@da.\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
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   "execution_count": null,
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      "e9dcbbb06e584549ba0adbf846ad8999",
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      "c050e033d5614e97a63b09513cb6eb10",
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      "3b81b38993c14de68df2ff40f0a4f590"
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    },
    "id": "SQaaqperccbA",
    "outputId": "f35afe70-71d0-498c-a08f-b2ba28b5e9a5"
   },
   "outputs": [],
   "source": [
    "# Case 4: RDF list\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:List1 ex:contents (\"one\" \"two\" \"three\").\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
     "referenced_widgets": [
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      "d4f26543a05649eab36d2d2e430bf868"
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    },
    "id": "r0kHJogZFEKO",
    "outputId": "790750bc-b180-4bf4-e847-ac279c59f5dc"
   },
   "outputs": [],
   "source": [
    "# Case 5: Blank nodes\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:bob ex:nationality _:c .\n",
    "_:c ex:country \"Canada\" .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
     "referenced_widgets": [
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   },
   "outputs": [],
   "source": [
    "# Case 6: Named graphs\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
    "\n",
    "ex:Monica ex:employer ex:ArangoDB .\n",
    "\n",
    "ex:Graph1 {\n",
    "    ex:Monica a ex:Entity .\n",
    "    ex:Management a ex:Skill .\n",
    "    ex:Monica ex:name \"Monica\" .\n",
    "    ex:Monica ex:homepage <http://www.Monicahompage.org> .\n",
    "    ex:Monica ex:hasSkill ex:Management .\n",
    "    ex:Monica ex:dateOfBirth \"1963-03-22\".\n",
    "}\n",
    "\n",
    "ex:Graph2 {\n",
    "    ex:Programming a ex:Skill .\n",
    "    <http://www.Monicahompage.org> a ex:Website .\n",
    "    ex:Monica a ex:Person .\n",
    "    ex:Person rdfs:subClassOf ex:Entity .\n",
    "    ex:Monica ex:hasSkill ex:Programming .\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
     "referenced_widgets": [
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    "id": "gtHKG7PiGyyF",
    "outputId": "5bd57d09-d663-4156-9df3-3725eeb33aee"
   },
   "outputs": [],
   "source": [
    "# Case 7: Multiple types for resources - rdf:type\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix : <http://example.com/> .\n",
    "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
    "@prefix adb: <http://www.arangodb.com/> .\n",
    "@prefix owl: <http://www.w3.org/2002/07/owl#> .\n",
    "\n",
    ":alice a :Arson .\n",
    ":alice a :Author .\n",
    "\n",
    ":Zenkey rdfs:subClassOf :Zebra .\n",
    ":Zenkey rdfs:subClassOf :Donkey .\n",
    ":Donkey rdfs:subClassOf :Animal .\n",
    ":Zebra rdfs:subClassOf :Animal .\n",
    ":Human rdfs:subClassOf :Animal .\n",
    ":Animal rdfs:subClassOf :LivingThing .\n",
    ":LivingThing rdfs:subClassOf :Thing .\n",
    ":Thing rdfs:subClassOf :Object .\n",
    "\n",
    ":charlie a :LivingThing .\n",
    ":charlie a :Animal .\n",
    ":charlie a :Zenkey .\n",
    "\n",
    ":marty a :LivingThing .\n",
    ":marty a :Animal .\n",
    ":marty a :Human .\n",
    ":marty a :Author .\n",
    "\n",
    ":john a :Singer .\n",
    ":john a :Writer .\n",
    ":john a :Guitarist .\n",
    ":john adb:collection \"Artist\" .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ZeZNno5k3XW"
   },
   "source": [
    "Cases 8 - 15: RDF-Star\n",
    "\n",
    "The [rdflib](https://github.com/RDFLib/rdflib) package hasn't introduced support for [Quoted Triples](https://www.w3.org/TR/rdf12-concepts/#dfn-quoted-triple) yet, so ArangoRDF's support for RDF-star is based on [Triple Reification](https://www.w3.org/wiki/RdfReification)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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    "id": "XevGMv7qdPgI",
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   },
   "outputs": [],
   "source": [
    "# Case 8:  Embedded object property statement in subject position\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:alice;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:bob ;\n",
    "    ex:certainty \"0.5\"^^xsd:double .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
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   "outputs": [],
   "source": [
    "# Case 9: Embedded datatype property statement in subject position\n",
    "# Note: PGT does not support this case yet\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:mark;\n",
    "    rdf:predicate ex:age;\n",
    "    rdf:object 28 ;\n",
    "    ex:certainty 1 .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184,
     "referenced_widgets": [
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   },
   "outputs": [],
   "source": [
    "# Case 10: Embedded object property statement in object position\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "_:x a rdf:Statement;\n",
    "    rdf:subject ex:mainPage;\n",
    "    rdf:predicate ex:writer;\n",
    "    rdf:object ex:alice;\n",
    "    ex:1 \"1\";\n",
    "    ex:2 \"2\";\n",
    "    ex:3 \"3\" .\n",
    "\n",
    "ex:bobshomepage ex:source _:x .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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   "outputs": [],
   "source": [
    "# Case 11: Embedded object property statement in subject position and non-literal object\n",
    "# Case 11.1: Asserted statement with non-literal object\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:mainPage;\n",
    "    rdf:predicate ex:writer;\n",
    "    rdf:object ex:alice ;\n",
    "    ex:source ex:bobshomepage .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184,
     "referenced_widgets": [
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      "e39bf2d80ac94271bdeabee0c0c3cbb1"
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    },
    "id": "0oZbDeLeS6ll",
    "outputId": "aae04df7-3caf-4278-f11f-5d0e1c243895"
   },
   "outputs": [],
   "source": [
    "# Case 11: Embedded object property statement in subject position and non-literal object\n",
    "# Case 11.2: Asserted statement with non-literal object that appears in another asserted statement\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "ex:alex ex:age 25 .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:alice;\n",
    "    rdf:predicate ex:friend;\n",
    "    rdf:object ex:bob ;\n",
    "    ex:mentionedBy ex:alex .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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      "bafb6c71b2174ce5a3b261d3c48cf0b4"
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    },
    "id": "woNfHiZ5S__t",
    "outputId": "b547b01d-724e-4c65-de21-68ccb631ca48"
   },
   "outputs": [],
   "source": [
    "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
    "# Case 12.1: Asserted statement with rdf:type as predicate\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:mainPage;\n",
    "    rdf:predicate ex:writer;\n",
    "    rdf:object ex:alice ;\n",
    "    rdf:type ex:bobshomepage .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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      "f711293be6064fbeb78a10e36fb4ca3c"
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    },
    "id": "lbbdb2lwS_2M",
    "outputId": "0b252df3-43ed-4a2d-d459-888207d45bf9"
   },
   "outputs": [],
   "source": [
    "# Case 12: Embedded statement in subject position - object property with rdf:type predicate\n",
    "# Case 12.2: Embedded statement with rdf:type as predicate\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:lara;\n",
    "    rdf:predicate rdf:type;\n",
    "    rdf:object ex:writer ;\n",
    "    ex:owner ex:journal .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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    "id": "4TwIzKZ4S_tN",
    "outputId": "b4f66a58-7857-411e-e900-faab135e9253"
   },
   "outputs": [],
   "source": [
    "# Case 13: Nested RDF-star statements\n",
    "# Case 13.1: Nested RDF-star statement in Subject position\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "ex:steve_position_ceo a rdf:Statement;\n",
    "    rdf:subject ex:steve;\n",
    "    rdf:predicate ex:position;\n",
    "    rdf:object ex:CEO .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:steve_position_ceo;\n",
    "    rdf:predicate ex:mentionedBy;\n",
    "    rdf:object ex:book;\n",
    "    ex:source ex:journal .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134,
     "referenced_widgets": [
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    "id": "BfMbv1NpP6nt",
    "outputId": "23bacd02-4a05-4e7b-9955-860ab70d9801"
   },
   "outputs": [],
   "source": [
    "# Case 13: Nested RDF-star statements\n",
    "# Case 13.2: Nested RDF-star statements in Object positions\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "ex:steve_position_ceo a rdf:Statement;\n",
    "    rdf:subject ex:steve;\n",
    "    rdf:predicate ex:position;\n",
    "    rdf:object ex:CEO .\n",
    "\n",
    "ex:book_mentioned_by_steve_position_ceo a rdf:Statement;\n",
    "    rdf:subject ex:book;\n",
    "    rdf:predicate ex:mentionedBy;\n",
    "    rdf:object ex:steve_position_ceo .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:journal;\n",
    "    rdf:predicate ex:source;\n",
    "    rdf:object ex:book_mentioned_by_steve_position_ceo .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
     "referenced_widgets": [
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    "id": "yZDLiPMkS_kG",
    "outputId": "ffc977ba-aa38-42fc-9859-517e63cc453e"
   },
   "outputs": [],
   "source": [
    "# Case 14: Multi-valued properties\n",
    "# Case 14.1: RDF statements with same subject and predicate and different objects\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "\n",
    "ex:college_page ex:subject \"Info_Page\";\n",
    "    ex:subject \"aau_page\" .\n",
    "\n",
    "ex:college_page_2 ex:subject \"Info_Page\" .\n",
    "\n",
    "ex:college_page ex:link ex:college_page_2 .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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    "id": "X_qBsxffS_br",
    "outputId": "0ba71c7c-e1ec-42d0-b82c-72835fac14ad"
   },
   "outputs": [],
   "source": [
    "# Case 14: Multi-valued properties\n",
    "# Case 14.2: RDF-star statements with the same subject and predicate and different objects\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt ;\n",
    "    ex:certainty \"0.5\"^^xsd:double .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt ;\n",
    "    ex:certainty 1 .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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      "70bb188603354ddca66d4146ec8e471c",
      "960cca7f5fe44b1db8d6f1b42ec9c96a",
      "d515e99b328d4e118174560fa329af02",
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      "99036c6715c544ea9c50cd68621fde3a",
      "680bf32f724e469982385088a61bb1fc",
      "29bd1e51d7a5416db6f8646bbf620040",
      "13356c7a8f8b4a83bfe2caa420745097",
      "a89b108c9fec490687b74e968dfe98f7",
      "cdd0250b550f438c863d72f4706d9131",
      "86fd50b8fd6f4dbf95b30557df477519",
      "b2b5e1788189446fa4ecab95341c4c20",
      "7e791f4fd0d54af68e2f96e3e3119830"
     ]
    },
    "id": "ZQqkFmPZQ69J",
    "outputId": "2cc05095-f116-49e4-e2f0-deb47d721d25"
   },
   "outputs": [],
   "source": [
    "# Case 14: Multi-valued properties\n",
    "# Case 14.3: Contexted within Named Graphs\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:college_page_3;\n",
    "    rdf:predicate ex:subject;\n",
    "    rdf:object \"Info_Page\" .\n",
    "\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:college_page_4;\n",
    "    rdf:predicate ex:subject;\n",
    "    rdf:object \"Info_Page\", \"aau_page\" .\n",
    "\n",
    "ex:Graph1 {\n",
    "    ex:college_page ex:subject \"Info_Page\";\n",
    "        ex:subject \"aau_page\" .\n",
    "}\n",
    "\n",
    "ex:Graph2 {\n",
    "    [] a rdf:Statement;\n",
    "        rdf:subject ex:college_page;\n",
    "        rdf:predicate ex:link;\n",
    "        rdf:object ex:college_page_3;\n",
    "        ex:foo \"bar\";\n",
    "        ex:foo \"bar2\" .\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
     "referenced_widgets": [
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    },
    "id": "cD-S3cZ-S_Ta",
    "outputId": "975b43e8-0151-44e2-cee8-34c160835cc7"
   },
   "outputs": [],
   "source": [
    "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
    "# Case 15.1: Basic Reified Statements\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt;\n",
    "    ex:certainty 0.5 .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt;\n",
    "    ex:source \"text\" .\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184,
     "referenced_widgets": [
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    "id": "r7jArqpvR6OK",
    "outputId": "4d118087-03ed-4d19-a20e-73960dd22fd5"
   },
   "outputs": [],
   "source": [
    "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
    "# Case 15.2: Basic Reified Statement + regular statement\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt;\n",
    "    ex:certainty \"0.5\"^^xsd:double ;\n",
    "    ex:foo ex:bar .\n",
    "\n",
    "ex:Mary ex:likes ex:Matt .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184,
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    "id": "oWZcONtcSFwb",
    "outputId": "16f97ebf-9a3c-4e16-dc1f-130c5a898287"
   },
   "outputs": [],
   "source": [
    "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
    "# Case 15.2: Basic Reified Statement + duplicate \"regular\" statement\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt;\n",
    "    ex:certainty \"0.5\"^^xsd:double ;\n",
    "    ex:foo ex:bar .\n",
    "\n",
    "ex:Mary ex:likes ex:Matt .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184,
     "referenced_widgets": [
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    "id": "b_H6fDD0STPw",
    "outputId": "61aebcda-78b1-4b02-daac-4d0ddd896062"
   },
   "outputs": [],
   "source": [
    "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
    "# Case 15.3: Basic Reified Statement + duplicate Reified Statement\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt;\n",
    "    ex:certainty \"0.5\"^^xsd:double ;\n",
    "    ex:foo ex:bar .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "    rdf:subject ex:Mary;\n",
    "    rdf:predicate ex:likes;\n",
    "    rdf:object ex:Matt .\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 167,
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    "id": "FQj1859cRwms",
    "outputId": "a8c7c579-7cb5-4f6d-d11a-13c661411d42"
   },
   "outputs": [],
   "source": [
    "# Case 15: Identical embedded RDF-star statements with different asserted statements\n",
    "# Case 15.4: Nested Reified Statements with Named Graphs\n",
    "\n",
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .\n",
    "\n",
    "[] a rdf:Statement;\n",
    "        rdf:subject ex:Mary;\n",
    "        rdf:predicate ex:likes;\n",
    "        rdf:object ex:Matt;\n",
    "        ex:certainty 1 .\n",
    "\n",
    "ex:Graph1 {\n",
    "    ex:mary_likes_matt_05 a rdf:Statement;\n",
    "        rdf:subject ex:Mary;\n",
    "        rdf:predicate ex:likes;\n",
    "        rdf:object ex:Matt;\n",
    "        ex:certainty 0.5 .\n",
    "}\n",
    "\n",
    "ex:Graph2 {\n",
    "    [] a rdf:Statement;\n",
    "        rdf:subject ex:Mary;\n",
    "        rdf:predicate ex:likes;\n",
    "        rdf:object ex:Matt;\n",
    "        ex:certainty 0.75 .\n",
    "\n",
    "    [] a rdf:Statement;\n",
    "        rdf:subject ex:John;\n",
    "        rdf:predicate ex:said;\n",
    "        rdf:object ex:mary_likes_matt_05;\n",
    "        ex:foo \"bar\" .\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "rpt_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data, True), overwrite_graph=True)\n",
    "pgt_graph = adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data, True), overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0SWi4e3wIMtw"
   },
   "source": [
    "#### Graph Contextualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vec21mb9MkhR"
   },
   "source": [
    "❗ Graph Contextualiztion is a work-in-progress feature ❗\n",
    "\n",
    "Contextualizing an RDF Graph to enhances the Terminology Box of the original RDF Graph. This is done by:\n",
    "\n",
    "1. Loading the OWL, RDF, and RDFS Ontologies into the RDF Graph\n",
    "2. Providing Domain & Range Inference\n",
    "3. Providing Domain & Range Introspection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 0,
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    "id": "P9oGi91RJbAI",
    "outputId": "c9097281-026d-400f-f245-f4aae639ee62"
   },
   "outputs": [],
   "source": [
    "data = \"\"\"\n",
    "PREFIX : <http://stardog.com/tutorial/>\n",
    "PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
    "PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n",
    "\n",
    ":The_Beatles      rdf:type  :Band .\n",
    ":The_Beatles      :name     \"The Beatles\" .\n",
    ":The_Beatles      :member   :John_Lennon .\n",
    ":The_Beatles      :member   :Paul_McCartney .\n",
    ":The_Beatles      :member   :Ringo_Starr .\n",
    ":The_Beatles      :member   :George_Harrison .\n",
    ":John_Lennon      rdf:type  :SoloArtist .\n",
    ":Paul_McCartney   rdf:type  :SoloArtist .\n",
    ":Ringo_Starr      rdf:type  :SoloArtist .\n",
    ":George_Harrison  rdf:type  :SoloArtist .\n",
    ":Please_Please_Me rdf:type  :Album .\n",
    ":Please_Please_Me :name     \"Please Please Me\" .\n",
    ":Please_Please_Me :date     \"1963-03-22\"^^xsd:date .\n",
    ":Please_Please_Me :artist   :The_Beatles .\n",
    ":Please_Please_Me :track    :Love_Me_Do .\n",
    ":Love_Me_Do       rdf:type  :Song .\n",
    ":Love_Me_Do       :name     \"Love Me Do\" .\n",
    ":Love_Me_Do       :length   125 .\n",
    ":Love_Me_Do       :writer   :John_Lennon .\n",
    ":Love_Me_Do       :writer   :Paul_McCartney .\n",
    "\n",
    ":McCartney        rdf:type  :Album .\n",
    ":McCartney        :name     \"McCartney\" .\n",
    ":McCartney        :date     \"1970-04-17\"^^xsd:date .\n",
    ":McCartney        :artist   :Paul_McCartney .\n",
    "\n",
    ":Imagine          rdf:type  :Album .\n",
    ":Imagine          :name     \"Imagine\" .\n",
    ":Imagine          :date     \"1971-10-11\"^^xsd:date .\n",
    ":Imagine          :artist   :John_Lennon .\n",
    "\"\"\"\n",
    "\n",
    "rpt_contextualized_graph = adbrdf.rdf_to_arangodb_by_rpt(\"DataRPT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)\n",
    "pgt_contextualized_graph= adbrdf.rdf_to_arangodb_by_pgt(\"DataPGT\", get_graph(data), contextualize_graph=True, overwrite_graph=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9gBg-hDs77i7"
   },
   "source": [
    "# <u>ArangoDB to RDF</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KSGKrcaPa_pi"
   },
   "source": [
    "We'll be using an ArangoDB-native dataset representing a Knowledge Graph constructed from the [GDETL Project](https://www.gdeltproject.org/).\n",
    "\n",
    "> GDELT monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day. [...] Put simply, the GDELT Project is a realtime open data global graph over human society as seen through the eyes of the world's news media, reaching deeply into local events, reaction, discourse, and emotions of the most remote corners of the world in near-realtime and making all of this available as an open data firehose to enable research over human society.\n",
    "\n",
    "The events we're using today range from peaceful protests to significant battles. The image below depicts the connections around an example event:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wtyqU1Wdb-jR"
   },
   "source": [
    "![open_intelligence_sample copy.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0,
     "referenced_widgets": [
      "0806477acc4149a980271b600b0b27e6",
      "8b258df3426f4781afed10499392c225",
      "7145e6a71760467eb8797864f0e5d2a0",
      "9c5380da6d794a228ac12ecab0d949ef",
      "e6256c3b21914adc871cf4db6baf7fee",
      "af651b267e5b4b11a2605b02f5a75ff3",
      "f86c993cec21489983df88cee2ab5b2a",
      "8348424c59c94f279f061d2ba339b17d",
      "5c2e8b16eaba46d7ad63a4723a0e6c95",
      "db764534dfec4bc3a9ef40f37c154129",
      "ff2e1f75ed3a42e9882c2ce5e5d748c9",
      "c4d4cc4bf5c54d6691135b25ba4d0c84",
      "57c5c601d72d4bd8a30779500c0f63f1",
      "678ff0b0994a40a982091b2d13f57c4a",
      "958d1cb2fff3485a86ad5f3d9456d579",
      "3fe5027f1c994fc4ba3c22f245604c13",
      "050f31f028794d49bdad2e33c5e1e32e",
      "a2954cc253fa4a2f9ad430ee12cec117",
      "1b06375f7c054d05aac5766732801ad9",
      "399256ab248e403ba3f26d5bd91b091c",
      "3c19457d425b4574817572b38aa17d86",
      "7a9a3f418d6a409cba2643daa11ec233",
      "28ace3f50b3c4a25a7fc1b7f6eab7814",
      "d2bc18ff98e142c58fdf18a9075d4c2b",
      "98d22f88193c47939b93d552a3041447",
      "66079ff3dbe043df86ba4c8cddd7ffb7",
      "245bdf024f754fa1bbb8bedc5b59cfef",
      "4f5490021107448cba0795253a892a71",
      "47f69ff14b9a4021bdfe15d532a65753",
      "3f92018b112e4213a1ee9b3d810828e5",
      "4f4bb6f685a44d318827eecfd9d25215",
      "a4acb870019b4b1daabbc92ce84ed88b",
      "c48a252f55d64e78aadc927d94de4b4f",
      "380f4a19bf51442e8bc93b77abe405d5",
      "798d0ce58f28445cb91bcd4af75ad379",
      "09a5785134684029b1cff1bc7510c534",
      "f280bf45be9947bfa83e5d2ad1ffdd48",
      "5b08d7a0cd37421fac9dd182c7e803a3",
      "0c409a70fd6b4c42a75e575398722229",
      "f74aa3ab9aff44f89396316e51f9774a",
      "354a8042212b42b5ab48116c07be8c4a",
      "ce982e0638b4403dba8bb46e8402d506",
      "5c32f4f07b8241eb810c6b7d3bf0d516",
      "98ef86cf824a4d7ca12d8cb76b22bbb6",
      "772165f70bd347009bdbdc989a3610e5",
      "e7e96edf087640c5a618deb016e4d277",
      "9a481076f28e4e9098fca0d75d5e44f3",
      "efcf3c8ce7424604b4726a5fdf8f9a97",
      "ac0b18ff7a1c40b5b50326d891f5e5af",
      "96e6a9c132cb4e068c01d819c9c53951",
      "87ccb50738954e6d94e2d75f8194215f",
      "096ad7b5b4844287b88f2b76e5cf107b",
      "5e4d0d5329cd4f8f83fcef7dfd413eb1",
      "d1ae873ccd9b459b8c8074d0cf87ff84",
      "820e68b984f6428a8bff72a178447b41",
      "d61489f2e3b34790905f0d2f5e32f17f",
      "a81ff8d9fc6142188423abf029103491",
      "ef3be25a72514885a169017b63a4212f"
     ]
    },
    "id": "a6uPF7X_a2sB",
    "outputId": "d43ae6b2-19b9-4399-9879-fd98f37f995e"
   },
   "outputs": [],
   "source": [
    "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
    "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
    "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7mNWLGGTUipj"
   },
   "source": [
    "#### via Graph Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0,
     "referenced_widgets": [
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      "80d8531d226f4b6c9d144aaea20130e3",
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      "b126e7213e4f4630af51f1c3fe526e58",
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      "3cbbfb7c4c254dd995bc2d10e2429a4d",
      "005f4ea3e18243d886f4264a8e328bcf"
     ]
    },
    "id": "rA5dBseDaiQB",
    "outputId": "6979c566-f6c2-4c41-d98c-183b1317d728"
   },
   "outputs": [],
   "source": [
    "rdf_graph = adbrdf.arangodb_graph_to_rdf(name=\"OPEN_INTELLIGENCE_ANGOLA\", rdf_graph=Graph())\n",
    "\n",
    "print(len(rdf_graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-FTakjQKUoWW"
   },
   "source": [
    "#### via Collection Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0,
     "referenced_widgets": [
      "ad0e243808cb46aa8605d8929e5129e2",
      "15f7cd15b0904731a4ce5daaa81f560b",
      "c49b5805f1bb4cd79fd87111fbd96118",
      "23f1b6f7b7564c78aaf32ed5b7f382a2",
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      "2f4743884cf64364831cd82a9f2b7ed3",
      "9222e80943b64fdaafbf0081aa501800",
      "d1bd0d1096f042db9f2e41892c8855ff",
      "67989ac8ad2a4f1a9919736d384cd294",
      "ee1ee485ba5d4618906048b066e5b6b9",
      "d6fa32c7e0794b78972d889c2ce55546",
      "45883547766c4981b798206e55a5c2f9",
      "95725b5bc05e4576b867b6066c5cd0b4",
      "6e3a7048cf634fe4b8d16547ffe89d7d",
      "5e4096f76e344148b57a688b2e9fe8b9",
      "0769e25469e44c0f97b9d1f9ad2abf5d",
      "50e0d72760df48cdbd0ab58a118f3b83",
      "00da493dbd004b858625ed4fb1e2556d",
      "48de9d4c770749dc933ec65ef35e9ce6"
     ]
    },
    "id": "TDny1v4sdelB",
    "outputId": "346d2e1a-02b9-4918-8370-a0305ab66f22"
   },
   "outputs": [],
   "source": [
    "rdf_graph = adbrdf.arangodb_collections_to_rdf(\n",
    "    name=\"OPEN_INTELLIGENCE_ANGOLA\",\n",
    "    rdf_graph=Graph(),\n",
    "    v_cols={\"Event\", \"Actor\", \"Source\"},\n",
    "    e_cols={\"eventActor\", \"hasSource\"},\n",
    ")\n",
    "\n",
    "print(len(rdf_graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qddfFNtaUpoo"
   },
   "source": [
    "#### via MetaGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0,
     "referenced_widgets": [
      "c09d4aa3bbb440eea986e872479dd746",
      "04e0e2fbdcbf43bdb6fd3a1cb1b9a5ba",
      "5c3399c807834637b5b872fdec6a126a",
      "a27c2cbe14eb40aa8b071a031c694220",
      "94e3ac81d4924b1699a1801aaffd5349",
      "6691ef61f8e743e8a274bf0710c84673",
      "85ee2b46acbe42cb838ff89ee8457607",
      "c627a1455abe47ac921d1bd841cdd9c2",
      "008a2c05cf294ef0a8be167b1a2433e9",
      "a6fb760fd98d448b990b6a9636df4894",
      "fe58b0d093f24f3da92c546038fe1735",
      "de3564cef3e44c2fbc0a25953d1b4500"
     ]
    },
    "id": "kQSN13xdeH1X",
    "outputId": "497885bc-1bdd-4a10-986f-387287b807cc"
   },
   "outputs": [],
   "source": [
    "rdf_graph = adbrdf.arangodb_to_rdf(\n",
    "    name=\"OPEN_INTELLIGENCE_ANGOLA\",\n",
    "    rdf_graph=Graph(),\n",
    "    metagraph={\n",
    "        \"vertexCollections\": {\n",
    "            \"Event\": {\"date\", \"description\", \"fatalities\"},\n",
    "            \"Actor\": {\"name\"}\n",
    "        },\n",
    "        \"edgeCollections\": {\n",
    "            \"eventActor\": {}\n",
    "        },\n",
    "    },\n",
    ")\n",
    "\n",
    "print(len(rdf_graph))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RGivoIz5Uv_-"
   },
   "source": [
    "# <u>Round-Tripping</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s900bHJQinK3",
    "outputId": "25e0dae1-e450-4576-aed3-38d1d4223dfa"
   },
   "outputs": [],
   "source": [
    "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
    "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
    "db.delete_graph(\"OPEN_INTELLIGENCE_ANGOLA\", drop_collections=True, ignore_missing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4PzrROxlU0TM"
   },
   "source": [
    "#### RDF -> ArangoDB -> RDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oZNYvQxDgTvw"
   },
   "outputs": [],
   "source": [
    "data = \"\"\"\n",
    "@prefix ex: <http://example.com/> .\n",
    "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
    "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
    "\n",
    "ex:Monica ex:employer ex:ArangoDB .\n",
    "\n",
    "ex:Graph1 {\n",
    "    ex:Monica a ex:Entity .\n",
    "    ex:Management a ex:Skill .\n",
    "    ex:Monica ex:name \"Monica\" .\n",
    "    ex:Monica ex:homepage <http://www.Monicahompage.org> .\n",
    "    ex:Monica ex:hasSkill ex:Management .\n",
    "    ex:Monica ex:dateOfBirth \"1963-03-22\".\n",
    "}\n",
    "\n",
    "ex:Graph2 {\n",
    "    ex:Programming a ex:Skill .\n",
    "    <http://www.Monicahompage.org> a ex:Website .\n",
    "    ex:Monica a ex:Person .\n",
    "    ex:Person rdfs:subClassOf ex:Entity .\n",
    "    ex:Monica ex:hasSkill ex:Programming .\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "rdf_graph_original = get_graph(data, True)\n",
    "\n",
    "def graphs_are_identical(rdf_graph_a: Graph, rdf_graph_b: Graph) -> Graph:\n",
    "    assert rdf_graph_a and rdf_graph_b\n",
    "    return len(rdf_graph_a - rdf_graph_b) == 0 and len(rdf_graph_b - rdf_graph_a) == 0"
   ]
  },
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   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "O94Dg2sVhTdT",
    "outputId": "76636c9e-6627-4940-9bfe-00c7210491a5"
   },
   "outputs": [],
   "source": [
    "# PGT\n",
    "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n",
    "    \"DataPGT\",\n",
    "    get_graph(data, True),\n",
    "    overwrite_graph=True,\n",
    ")\n",
    "\n",
    "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n",
    "    \"DataPGT\",\n",
    "    type(rdf_graph_original)()\n",
    ")\n",
    "\n",
    "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168,
     "referenced_widgets": [
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      "f4e4607b23a54074b058c823703b1c53",
      "dd28a24eec3d4539bfc5be068fdb4717",
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      "ae8c90ad5c7d42c4b702373327e843a6",
      "64ef034cf3e043359fa8a92e4c040f94",
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      "87ee460987eb400ba53ef8a8ac0f83ec",
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      "f5d10a6ad6124b15829f3d9d64880c79",
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      "91ae98fc5b2e451ba177255edf131409",
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      "16b2c93f497d4f62aa06a35052933fd1",
      "7fe6e86bf2d942c293ee3fdd97dc863a",
      "2741d4016cd94f18ac0d38c5f16de281"
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    },
    "id": "Y51zcrJUfiL5",
    "outputId": "30def815-e95f-4930-b6bc-9da664d748cf"
   },
   "outputs": [],
   "source": [
    "# RPT\n",
    "adb_graph = adbrdf.rdf_to_arangodb_by_rpt(\n",
    "    \"DataRPT\",\n",
    "    get_graph(data, True),\n",
    "    overwrite_graph=True,\n",
    ")\n",
    "\n",
    "rdf_graph_new = adbrdf.arangodb_graph_to_rdf(\n",
    "    \"DataRPT\",\n",
    "    type(rdf_graph_original)()\n",
    ")\n",
    "\n",
    "assert graphs_are_identical(rdf_graph_original, rdf_graph_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_q34B2SQU22_"
   },
   "source": [
    "#### ArangoDB -> RDF -> ArangoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 260,
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    },
    "id": "tCBsS5Q2jXrP",
    "outputId": "f528702b-7f75-4be4-ed21-3fc339da1d57"
   },
   "outputs": [],
   "source": [
    "db.delete_graph(\"DataRPT\", drop_collections=True, ignore_missing=True)\n",
    "db.delete_graph(\"DataPGT\", drop_collections=True, ignore_missing=True)\n",
    "\n",
    "Datasets(db).load(\"OPEN_INTELLIGENCE_ANGOLA\")\n",
    "\n",
    "\n",
    "def get_adb_graph_count(name: str):\n",
    "    global db\n",
    "    adb_graph = db.graph(name)\n",
    "\n",
    "    e_cols = {col[\"edge_collection\"] for col in adb_graph.edge_definitions()}\n",
    "\n",
    "    v_count = 0\n",
    "    for v in db.graph(name).vertex_collections():\n",
    "        if v in e_cols:\n",
    "            continue\n",
    "\n",
    "        v_count += adb_graph.vertex_collection(v).count()\n",
    "\n",
    "    e_count = 0\n",
    "    for e_d in adb_graph.edge_definitions():\n",
    "        e_count += adb_graph.edge_collection(e_d[\"edge_collection\"]).count()\n",
    "\n",
    "    return (v_count, e_count)\n",
    "\n",
    "original_v_count, original_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    },
    "id": "K0ZN58bEjYFk",
    "outputId": "4da5197e-fde3-4389-833d-e760aafed948"
   },
   "outputs": [],
   "source": [
    "# PGT\n",
    "rdf_graph = adbrdf.arangodb_graph_to_rdf(\n",
    "    \"OPEN_INTELLIGENCE_ANGOLA\",\n",
    "    Graph(),\n",
    "    list_conversion_mode=\"serialize\",\n",
    "    dict_conversion_mode=\"serialize\",\n",
    "    include_adb_v_col_statements=True,\n",
    "    include_adb_v_key_statements=True,\n",
    "    include_adb_e_key_statements=True,\n",
    ")\n",
    "\n",
    "\n",
    "adb_graph = adbrdf.rdf_to_arangodb_by_pgt(\n",
    "    \"OPEN_INTELLIGENCE_ANGOLA\",\n",
    "    rdf_graph,\n",
    "    overwrite_graph=True\n",
    ")\n",
    "\n",
    "property_v_count = adb_graph.vertex_collection(\"Property\").count()\n",
    "\n",
    "new_v_count, new_e_count = get_adb_graph_count(\"OPEN_INTELLIGENCE_ANGOLA\")\n",
    "\n",
    "assert original_v_count == new_v_count - property_v_count\n",
    "assert original_e_count == new_e_count"
   ]
  }
 ],
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