KnowledgeValidation.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdflib import Graph, Literal, term, URIRef, RDF\n",
    "from rdflib.namespace import Namespace, NamespaceManager\n",
    "import pyshacl\n",
    "import re\n",
    "import os\n",
    "from rdflib.namespace import Namespace, NamespaceManager\n",
    "from NLP2SHACL import write_shacl"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Graph identifier=N4d0acbf075c3401e971c9b9570864258 (<class 'rdflib.graph.Graph'>)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data graph\n",
    "data_graph = Graph()\n",
    "data_graph.parse(\"InstanceModel/brick_model.ttl\", format=\"turtle\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validate_graph(data_graph, shape_graph):\n",
    "    shapes_file_format = 'turtle'\n",
    "    data_file_format = 'turtle'\n",
    "\n",
    "    res = pyshacl.validate(data_graph, shacl_graph = shape_graph,\n",
    "                                     data_graph_format = data_file_format,\n",
    "                                     shacl_graph_format = shapes_file_format,\n",
    "                                     inference=None)\n",
    "\n",
    "    return res\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BOT GUID Example\n",
    "Requirement:\n",
    "Each bot:Space must have one and only guid and that guid must be a string. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Shape File\n",
    "shape_graph = Graph()\n",
    "shape_graph.parse(\"Shape/ifc_guid.ttl\", format=\"turtle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = validate_graph(data_graph, shape_graph)\n",
    "res[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the invalid data graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_graph_invalid = Graph()\n",
    "data_graph_invalid.parse(\"InstanceModel/brick_model_invalid.ttl\", format=\"turtle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = validate_graph(data_graph_invalid, shape_graph)\n",
    "res[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brick VAV Example (point count)\n",
    "Each brick:VAV must have two Points."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using NLP2SHACL tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the SHACL\n",
    "requirement_txt= \"\"\"\n",
    "   R1 : Every HVAC_Zone should feeds by at least 1 VAV.\n",
    "   \"\"\"\n",
    "\n",
    "shacl_txt = write_shacl(requirement_txt)\n",
    "\n",
    "print(shacl_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the generated shacl text\n",
    "shape_graph = shacl_txt\n",
    "res = validate_graph(data_graph, shape_graph)\n",
    "res[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the hand-made SHACL shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Shape File\n",
    "shape_graph = Graph()\n",
    "shape_graph.parse(\"Shape/brick_vav_points_count.ttl\", format=\"turtle\")\n",
    "\n",
    "res = validate_graph(data_graph, shape_graph)\n",
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = validate_graph(data_graph_invalid, shape_graph)\n",
    "res[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brick VAV Point Type Example\n",
    "\n",
    "Each brick:VAV must have a brick:Zone_Air_Temperature_Sensor and a brick:Zone_Air_Temperature_Setpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Shape File\n",
    "shape_graph = Graph()\n",
    "shape_graph.parse(\"Shape/brick_vav_points_shape.ttl\", format=\"turtle\")\n",
    "\n",
    "res = validate_graph(data_graph, shape_graph)\n",
    "res[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = validate_graph(data_graph_invalid, shape_graph)\n",
    "res[0]"
   ]
  }
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