752 lines
24 KiB
Plaintext
752 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "747b245f",
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"metadata": {},
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"source": [
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"Build the Lego Knowledge Graph using the sources in `/data`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "90209948",
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"metadata": {},
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"outputs": [],
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"source": [
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"from rdflib import Graph, Namespace, XSD, OWL, RDF, RDFS, SKOS, URIRef, Literal\n",
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"import pandas as pd\n",
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"from datetime import datetime\n",
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"import os"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fe91fa67",
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"metadata": {},
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"source": [
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"Setup the requirements for building a knowledge graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8e573135",
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"metadata": {},
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"outputs": [],
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"source": [
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"g = Graph()\n",
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"thm = Namespace(\"https://thm.de/\")\n",
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"THM = Namespace(\"https://thm.de/ont/\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "class_hierarchy",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Graph identifier=Ne25ca93489ee450c8158409bd5d2a548 (<class 'rdflib.graph.Graph'>)>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Class hierarchy\n",
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"g.add((THM.LegoEntity, RDF.type, OWL.Class))\n",
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"g.add((THM.LegoEntity, RDFS.subClassOf, OWL.Thing))\n",
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"\n",
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"g.add((THM.PhysicalItem, RDF.type, OWL.Class))\n",
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"g.add((THM.PhysicalItem, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.Set, RDF.type, OWL.Class))\n",
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"g.add((THM.Set, RDFS.subClassOf, THM.PhysicalItem))\n",
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"g.add((THM.Part, RDF.type, OWL.Class))\n",
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"g.add((THM.Part, RDFS.subClassOf, THM.PhysicalItem))\n",
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"g.add((THM.Minifigure, RDF.type, OWL.Class))\n",
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"g.add((THM.Minifigure, RDFS.subClassOf, THM.PhysicalItem))\n",
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"\n",
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"g.add((THM.CatalogEntry, RDF.type, OWL.Class))\n",
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"g.add((THM.CatalogEntry, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.Color, RDF.type, OWL.Class))\n",
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"g.add((THM.Color, RDFS.subClassOf, THM.CatalogEntry))\n",
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"g.add((THM.PartCategory, RDF.type, OWL.Class))\n",
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"g.add((THM.PartCategory, RDFS.subClassOf, THM.CatalogEntry))\n",
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"g.add((THM.Theme, RDF.type, OWL.Class))\n",
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"g.add((THM.Theme, RDFS.subClassOf, THM.CatalogEntry))\n",
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"\n",
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"g.add((THM.Inventory, RDF.type, OWL.Class))\n",
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"g.add((THM.Inventory, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.InventoryEntry, RDF.type, OWL.Class))\n",
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"g.add((THM.InventoryEntry, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.InventoryPart, RDF.type, OWL.Class))\n",
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"g.add((THM.InventoryPart, RDFS.subClassOf, THM.InventoryEntry))\n",
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"g.add((THM.InventorySet, RDF.type, OWL.Class))\n",
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"g.add((THM.InventorySet, RDFS.subClassOf, THM.InventoryEntry))\n",
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"g.add((THM.InventoryMinifig, RDF.type, OWL.Class))\n",
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"g.add((THM.InventoryMinifig, RDFS.subClassOf, THM.InventoryEntry))\n",
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"\n",
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"g.add((THM.Producer, RDF.type, OWL.Class))\n",
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"g.add((THM.Producer, RDFS.subClassOf, THM.LegoEntity))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d56199d5",
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"metadata": {},
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"source": [
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"# Rebrickable"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1e1abb0",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d8a1fe84",
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"metadata": {},
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"outputs": [],
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"source": [
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"re_colors = pd.read_csv(\"data/rebrickable/colors.csv\")\n",
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"re_elements = pd.read_csv(\"data/rebrickable/elements.csv\")\n",
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"re_inventories = pd.read_csv(\"data/rebrickable/inventories.csv\")\n",
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"re_inventory_minifigs = pd.read_csv(\"data/rebrickable/inventory_minifigs.csv\")\n",
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"re_inventory_parts = pd.read_csv(\"data/rebrickable/inventory_parts.csv\")\n",
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"re_inventory_sets = pd.read_csv(\"data/rebrickable/inventory_sets.csv\")\n",
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"re_minifigs = pd.read_csv(\"data/rebrickable/minifigs.csv\")\n",
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"re_part_categories = pd.read_csv(\"data/rebrickable/part_categories.csv\")\n",
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"re_part_relationships = pd.read_csv(\"data/rebrickable/part_relationships.csv\")\n",
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"re_parts = pd.read_csv(\"data/rebrickable/parts.csv\")\n",
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"re_sets = pd.read_csv(\"data/rebrickable/sets.csv\")\n",
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"re_themes = pd.read_csv(\"data/rebrickable/themes.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3677416",
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"metadata": {},
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"source": [
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"Colors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "ae505704",
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"metadata": {},
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"outputs": [],
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"source": [
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"for color in re_colors.itertuples(index=False):\n",
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" color_ref = thm[f\"color/{color.id}\"]\n",
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"\n",
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" g.add((color_ref, RDF.type, THM.Color))\n",
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" g.add((color_ref, RDFS.label, Literal(color.name, lang=\"en\")))\n",
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" g.add((color_ref, THM.color, Literal(color.rgb)))\n",
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" g.add((color_ref, THM.is_transparent, Literal(color.is_trans, datatype=XSD.boolean)))\n",
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" \n",
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" if not pd.isna(color.y1):\n",
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" g.add((color_ref, THM.first_year, Literal(datetime(year = int(color.y1), month=1, day=1))))\n",
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" if not pd.isna(color.y2):\n",
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" g.add((color_ref, THM.last_year, Literal(datetime(year = int(color.y2), month=1, day=1))))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e27b2bc4",
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"metadata": {},
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"source": [
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"Part Categories"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fb9e17d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"for part_category in re_part_categories.itertuples(index=False):\n",
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" part_category_ref = thm[f\"part_category/{part_category.id}\"]\n",
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"\n",
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" g.add((part_category_ref, RDF.type, THM.PartCategory))\n",
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" g.add((part_category_ref, RDFS.label, Literal(part_category.name, lang=\"en\")))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ea32849b",
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"metadata": {},
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"source": [
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"Parts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "8fdb080e",
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"metadata": {},
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"outputs": [],
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"source": [
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"for part in re_parts.itertuples(index=False):\n",
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" part_ref = thm[f\"part/{part.part_num}\"]\n",
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"\n",
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" g.add((part_ref, RDF.type, THM.Part))\n",
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" g.add((part_ref, RDFS.label, Literal(part.name, lang=\"en\")))\n",
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" g.add((part_ref, THM.part_category, thm[f\"part_category/{part.part_cat_id}\"]))\n",
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" g.add((part_ref, THM.part_material, Literal(part.part_material)))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fcaadd84",
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"metadata": {},
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"source": [
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"Elements"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "579b1d67",
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"metadata": {},
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"outputs": [],
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"source": [
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"for element in re_elements.itertuples(index=False):\n",
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" part_ref = thm[f\"part/{element.part_num}\"]\n",
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" color_ref = thm[f\"color/{element.color_id}\"]\n",
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"\n",
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" g.add((part_ref, THM.has_color, color_ref))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "44dae336",
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"metadata": {},
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"source": [
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"Part Relationships"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "00db079a",
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"metadata": {},
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"outputs": [],
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"source": [
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"for part_relationship in re_part_relationships.itertuples(index=False):\n",
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" part_ref_parent = thm[f\"part/{part_relationship.parent_part_num}\"]\n",
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" part_ref_child = thm[f\"part/{part_relationship.child_part_num}\"]\n",
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"\n",
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" g.add((part_ref_parent, THM.has_child, part_ref_child))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19dc64b8",
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"metadata": {},
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"source": [
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"Themes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "1a529aae",
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"metadata": {},
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"outputs": [],
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"source": [
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"for theme in re_themes.itertuples(index=False):\n",
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" theme_ref = thm[f\"theme/{int(theme.id)}\"]\n",
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"\n",
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" g.add((theme_ref, RDF.type, THM.Theme))\n",
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" g.add((theme_ref, RDFS.label, Literal(theme.name, lang=\"en\")))\n",
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"\n",
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" if not pd.isna(theme.parent_id):\n",
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" g.add((theme_ref, THM.parent_theme, thm[f\"theme/{int(theme.parent_id)}\"]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3f72c2e9",
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"metadata": {},
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"source": [
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"Sets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "29b357ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"for lego_set in re_sets.itertuples(index=False):\n",
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" set_ref = thm[f\"set/lego/{lego_set.set_num}\"]\n",
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"\n",
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" g.add((set_ref, RDF.type, THM.Set))\n",
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" g.add((set_ref, RDFS.label, Literal(lego_set.name, lang=\"en\")))\n",
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" g.add((set_ref, THM.year, Literal(datetime(int(lego_set.year), 1, 1))))\n",
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" g.add((set_ref, THM.theme, thm[f\"theme/{int(lego_set.theme_id)}\"]))\n",
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" g.add((set_ref, THM.num_parts, Literal(int(lego_set.num_parts), datatype=XSD.integer)))\n",
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" g.add((set_ref, THM.brand, Literal(\"Lego\")))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d2616476",
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"metadata": {},
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"source": [
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"Minifigures"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "a67b3e70",
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"metadata": {},
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"outputs": [],
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"source": [
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"for minifig in re_minifigs.itertuples(index=False):\n",
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" minifig_ref = thm[f\"minifig/{minifig.fig_num}\"]\n",
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"\n",
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" g.add((minifig_ref, RDF.type, THM.Minifigure))\n",
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" g.add((minifig_ref, RDFS.label, Literal(minifig.name, lang=\"en\")))\n",
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" g.add((minifig_ref, THM.num_parts, Literal(int(minifig.num_parts), datatype=XSD.integer)))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e9baff1",
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"metadata": {},
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"source": [
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"Now the ugly part: Inventories"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "0c97dc4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"for inventory in re_inventories.itertuples(index=False):\n",
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" inventory_ref = thm[f\"inventory/{inventory.id}\"]\n",
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"\n",
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" g.add((inventory_ref, RDF.type, THM.Inventory))\n",
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" g.add((inventory_ref, THM.set, thm[f\"set/lego/{inventory.set_num}\"]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c962cf0",
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"metadata": {},
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"source": [
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"Inventories relate sets, minifigures and parts to each other, creating a kind of \"top level set\" \n",
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"(this takes a lot of time)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "dc2ba03e",
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"metadata": {},
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"outputs": [],
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"source": [
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"for inventory_part in re_inventory_parts.itertuples(index=False):\n",
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" inventory_part_ref = thm[f\"inventory_part/{inventory_part.inventory_id}/{inventory_part.part_num}\"]\n",
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"\n",
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" g.add((inventory_part_ref, RDF.type, THM.InventoryPart))\n",
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" g.add((inventory_part_ref, THM.inventory, thm[f\"inventory/{inventory_part.inventory_id}\"]))\n",
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" g.add((inventory_part_ref, THM.part, thm[f\"part/{inventory_part.part_num}\"]))\n",
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" g.add((inventory_part_ref, THM.quantity, Literal(int(inventory_part.quantity), datatype=XSD.integer)))\n",
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" g.add((inventory_part_ref, THM.is_spare, Literal(inventory_part.is_spare, datatype=XSD.boolean)))\n",
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" g.add((inventory_part_ref, THM.color, thm[f\"color/{inventory_part.color_id}\"]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "8715a1cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"for inventory_set in re_inventory_sets.itertuples(index=False):\n",
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" inventory_set_ref = thm[f\"inventory_set/{inventory_set.inventory_id}/{inventory_set.set_num}\"]\n",
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"\n",
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" g.add((inventory_set_ref, RDF.type, THM.InventorySet))\n",
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" g.add((inventory_set_ref, THM.inventory, thm[f\"inventory/{inventory_set.inventory_id}\"]))\n",
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" g.add((inventory_set_ref, THM.set, thm[f\"set/lego/{inventory_set.set_num}\"]))\n",
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" g.add((inventory_set_ref, THM.quantity, Literal(int(inventory_set.quantity), datatype=XSD.integer)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "08c2c580",
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"metadata": {},
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"outputs": [],
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"source": [
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"for inventory_minifig in re_inventory_minifigs.itertuples(index=False):\n",
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" inventory_minifig_ref = thm[f\"inventory_minifig/{inventory_minifig.inventory_id}/{inventory_minifig.fig_num}\"]\n",
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"\n",
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" g.add((inventory_minifig_ref, RDF.type, THM.InventoryMinifig))\n",
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" g.add((inventory_minifig_ref, THM.inventory, thm[f\"inventory/{inventory_minifig.inventory_id}\"]))\n",
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" g.add((inventory_minifig_ref, THM.minifig, thm[f\"minifig/{inventory_minifig.fig_num}\"]))\n",
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" g.add((inventory_minifig_ref, THM.quantity, Literal(int(inventory_minifig.quantity), datatype=XSD.integer)))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dcbab237",
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"metadata": {},
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"source": [
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"# Brickset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d8fb5374",
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"metadata": {},
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"source": [
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"add for prices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "1e0ac437",
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"metadata": {},
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"outputs": [],
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"source": [
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"bs_sets = pd.read_csv(\"./data/brickset/sets.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "fd944ccb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for bs_set in bs_sets.itertuples(index=False):\n",
|
|
" num = f\"{str(bs_set.Number).strip()}-{str(bs_set.Variant)}\" #Error for Set 853357\n",
|
|
" set_ref = thm[f\"set/lego/{num}\"]\n",
|
|
"\n",
|
|
" if (set_ref, None, None) in g:\n",
|
|
" if not pd.isna(bs_set.USRetailPrice):\n",
|
|
" g.add((set_ref, THM.us_retail_price, Literal(bs_set.USRetailPrice, datatype=XSD.float)))\n",
|
|
" if not pd.isna(bs_set.DERetailPrice):\n",
|
|
" g.add((set_ref, THM.de_retail_price, Literal(bs_set.DERetailPrice, datatype=XSD.float)))\n",
|
|
" if not pd.isna(bs_set.UKRetailPrice):\n",
|
|
" g.add((set_ref, THM.us_retail_price, Literal(bs_set.UKRetailPrice, datatype=XSD.float)))\n",
|
|
" if not pd.isna(bs_set.CARetailPrice):\n",
|
|
" g.add((set_ref, THM.ca_retail_price, Literal(bs_set.CARetailPrice, datatype=XSD.float)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a16fd51a",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Bricklink\n",
|
|
"\n",
|
|
"Add missing minifigs, parts and sets using bricklink data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "a8beb593",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bl_minifigs = pd.read_csv(\"./data/bricklink/minifigs.csv\")\n",
|
|
"bl_sets = pd.read_csv(\"./data/bricklink/sets.csv\")\n",
|
|
"bl_parts = pd.read_csv(\"./data/bricklink/parts.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "b14e6e89",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"additional_entries = 0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "bbf5462b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for bl_set in bl_sets.itertuples(index=False):\n",
|
|
" set_ref = thm[f\"set/lego/{bl_set.set_id}\"]\n",
|
|
"\n",
|
|
" if not (set_ref, None, None) in g:\n",
|
|
" additional_entries += 1\n",
|
|
" g.add((set_ref, RDF.type, THM.Set))\n",
|
|
" g.add((set_ref, RDFS.label, Literal(bl_set.set_name, lang=\"en\")))\n",
|
|
" if not pd.isna(bl_set.year) and str(bl_set.year).isnumeric():\n",
|
|
" g.add((set_ref, THM.first_year, Literal(datetime(int(bl_set.year), 1, 1))))\n",
|
|
" if not pd.isna(bl_set.parts) and str(bl_set.parts).isnumeric():\n",
|
|
" g.add((set_ref, THM.num_parts, Literal(int(bl_set.parts), datatype=XSD.integer)))\n",
|
|
" g.add((set_ref, THM.brand, Literal(\"Lego\")))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "ef52582e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for bl_part in bl_parts.itertuples(index=False):\n",
|
|
" part_ref = thm[f\"part/{bl_part.part_id}\"]\n",
|
|
"\n",
|
|
" if not (part_ref, None, None) in g:\n",
|
|
" additional_entries += 1\n",
|
|
" g.add((part_ref, RDF.type, THM.Part))\n",
|
|
" g.add((part_ref, RDFS.label, Literal(bl_part.part_name, lang=\"en\")))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "8bf0ffeb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for bl_minifig in bl_minifigs.itertuples(index=False):\n",
|
|
" minifig_ref = thm[f\"minifig/{bl_minifig.minifig_id}\"]\n",
|
|
"\n",
|
|
" if not (minifig_ref, None, None) in g:\n",
|
|
" additional_entries += 1\n",
|
|
" g.add((minifig_ref, RDF.type, THM.Minifigure))\n",
|
|
" g.add((minifig_ref, RDFS.label, Literal(bl_minifig.minifig_name, lang=\"en\")))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "3491b032",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Added 107748 items\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"Added {additional_entries} items\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6eca7f24",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Merlin"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "c1e9ff32",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"merlin_sets = pd.read_csv(\"./data/merlin/others.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "217dc4d2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"additional_entries = 0\n",
|
|
"\n",
|
|
"for merlin_set in merlin_sets.itertuples(index=False):\n",
|
|
" set_ref = thm[f\"set/lego/{merlin_set.id}\"]\n",
|
|
" producer_ref = thm[f\"producer/{merlin_set.producer}\"]\n",
|
|
"\n",
|
|
" if not (producer_ref, None, None) in g:\n",
|
|
" g.add((producer_ref, RDF.type, THM.Producer))\n",
|
|
" g.add((producer_ref, RDFS.label, Literal(merlin_set.producer)))\n",
|
|
"\n",
|
|
" if not (set_ref, None, None) in g:\n",
|
|
" additional_entries += 1\n",
|
|
" g.add((set_ref, RDF.type, THM.Set))\n",
|
|
" g.add((set_ref, RDFS.label, Literal(merlin_set.name)))\n",
|
|
" g.add((set_ref, THM.brand, producer_ref))\n",
|
|
" if not pd.isna(merlin_set.year):\n",
|
|
" g.add((set_ref, THM.first_year, Literal(int(merlin_set.year), datatype=XSD.integer)))\n",
|
|
" if not pd.isna(merlin_set.parts):\n",
|
|
" g.add((set_ref, THM.num_parts, Literal(int(merlin_set.parts), datatype=XSD.integer)))\n",
|
|
" g.add((set_ref, THM.size_word, Literal(merlin_set.size)))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "54eaa79e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"merlin_prices = pd.read_csv(\"./data/merlin/prices.csv\")\n",
|
|
"\n",
|
|
"for merlin_price in merlin_prices.itertuples(index=False):\n",
|
|
" set_ref = thm[f\"set/lego/{merlin_price.id}\"]\n",
|
|
" if merlin_price.price_eur not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.eur_price, Literal(merlin_price.price_eur, datatype=XSD.float)))\n",
|
|
" #usd (data src not clean)\n",
|
|
" if merlin_price._3 not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.usd_price, Literal(merlin_price._3, datatype=XSD.float)))\n",
|
|
" # cn \n",
|
|
" if merlin_price._2 not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.cn_price, Literal(merlin_price._2, datatype=XSD.float)))\n",
|
|
" # best price euro \n",
|
|
" if merlin_price._4 not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.eur_bestprice, Literal(merlin_price._4, datatype=XSD.float)))\n",
|
|
" # best price cn\n",
|
|
" if merlin_price._5 not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.cn_bestprice, Literal(merlin_price._5, datatype=XSD.float)))\n",
|
|
" # best price usd\n",
|
|
" if merlin_price._6 not in [None, \"_\"]:\n",
|
|
" g.add((set_ref, THM.usd_bestprice, Literal(merlin_price._6, datatype=XSD.float)))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bfab0c73",
|
|
"metadata": {},
|
|
"source": [
|
|
"Serialize the graph in turtle format"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2abd6894",
|
|
"metadata": {},
|
|
"source": [
|
|
"```\n",
|
|
" ___-------___\n",
|
|
" _-~~ ~~-_\n",
|
|
" _-~ /~-_\n",
|
|
" /^\\__/^\\ /~ \\ / \\\n",
|
|
" /| O|| O| / \\_______________/ \\\n",
|
|
"| |___||__| / / \\ \\\n",
|
|
"| \\ / / \\ \\\n",
|
|
"| (_______) /______/ \\_________ \\\n",
|
|
"| / / \\ / \\\n",
|
|
" \\ \\^\\\\ \\ / \\ /\n",
|
|
" \\ || \\______________/ _-_ //\\__//\n",
|
|
" \\ ||------_-~~-_ ------------- \\ --/~ ~\\ || __/\n",
|
|
" ~-----||====/~ |==================| |/~~~~~\n",
|
|
" (_(__/ ./ / \\_\\ \\.\n",
|
|
" (_(___/ \\_____)_)\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "1a30bff8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Graph identifier=Ne25ca93489ee450c8158409bd5d2a548 (<class 'rdflib.graph.Graph'>)>"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"g.bind(\"thmont\", THM)\n",
|
|
"\n",
|
|
"g.serialize(\"lego_graph_rebrickable.ttl\", format=\"turtle\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "7b9c3bcf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/3626cpr3530'), rdflib.term.Literal('Minifig Head Chitauri, Gold Armor, Lime Eyes, Large Open Mouth with Teeth Print', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n",
|
|
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/970c12'), rdflib.term.Literal('Hips and Dark Bluish Gray Legs', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n",
|
|
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/973c14h14pr5739'), rdflib.term.Literal('Torso Armor, Light Bluish Gray and Olive Green Panels Print, Light Bluish Gray Arms and Hands', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import rdflib\n",
|
|
" \n",
|
|
"query = \"\"\"\n",
|
|
"SELECT ?inventory_part ?part_name ?quantity\n",
|
|
"WHERE {\n",
|
|
" ?inventory thmont:set <https://thm.de/set/lego/fig-011397> .\n",
|
|
" ?inventory_part thmont:inventory ?inventory ;\n",
|
|
" thmont:part ?part ;\n",
|
|
" thmont:quantity ?quantity .\n",
|
|
" ?part rdfs:label ?part_name .\n",
|
|
"}\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"qres = g.query(query)\n",
|
|
"\n",
|
|
"for row in qres:\n",
|
|
" print(row)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "kgr",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|