knowledge graph construction with rebrickable data
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*.ttl
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{
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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 Knwoledge 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": 40,
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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"
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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": 41,
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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://th-mannheim.de/\")\n",
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"THM = Namespace(\"https://th-mannheim.de/ont/\")"
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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": 42,
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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": 43,
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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\"colors/{color.id}\"]\n",
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"\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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" # First appearance\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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" # Last appearance\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": 44,
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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, RDFS.label, Literal(part_category_ref, 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": 45,
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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, 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": 46,
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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\"colors/{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": 47,
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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": null,
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"id": "1a529aae",
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"metadata": {},
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"outputs": [
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{
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"ename": "SyntaxError",
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"evalue": "f-string: unmatched ')' (1024367582.py, line 2)",
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"output_type": "error",
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"traceback": [
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" \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[48]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mtheme_ref = thm[f\"theme/{int(theme.id))}\"]\u001b[39m\n ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m f-string: unmatched ')'\n"
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]
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}
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],
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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, RDFS.label, Literal(theme.name)))\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": "bfab0c73",
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"metadata": {},
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"source": [
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"Serialize the graph in turtle format"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2abd6894",
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"metadata": {},
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"source": [
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"```\n",
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" ___-------___\n",
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" _-~~ ~~-_\n",
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" _-~ /~-_\n",
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" /^\\__/^\\ /~ \\ / \\\n",
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" /| O|| O| / \\_______________/ \\\n",
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"| |___||__| / / \\ \\\n",
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"| \\ / / \\ \\\n",
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"| (_______) /______/ \\_________ \\\n",
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"| / / \\ / \\\n",
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" \\ \\^\\\\ \\ / \\ /\n",
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" \\ || \\______________/ _-_ //\\__//\n",
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" \\ ||------_-~~-_ ------------- \\ --/~ ~\\ || __/\n",
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" ~-----||====/~ |==================| |/~~~~~\n",
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" (_(__/ ./ / \\_\\ \\.\n",
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" (_(___/ \\_____)_)\n",
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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": null,
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"id": "1a30bff8",
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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=Nf661b2e682c043188ddd822a6bca246c (<class 'rdflib.graph.Graph'>)>"
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]
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},
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"execution_count": 36,
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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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"g.bind(\"thmont\", THM)\n",
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"\n",
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"g.serialize(\"lego_graph.ttl\", format=\"turtle\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv (3.14.4)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -142,7 +142,7 @@
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\subsection{Merlins Steine}
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\textit{Merlins Steine} ist eine Website, die sich auf die Berichterstattung von Reviews zu Klemmbausteinen spezifiziert. Um auf entsprechende Sets zu referenzieren beinhaltet \textit{Merlins Steine} eine Datenbank an Klemmbausteinen, die über 30000 Sets unterschiedlicher Marken enthält. Merlins Steine finanziert sich über Affiliate Marketing. \cite{SetDatenbankSetDB}.\\
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\textit{Merlins Steine} wurde ausgewählt, da Sets von anderen Marken im Vergleich zu vorherig genannten Quellen aufgelistet werden.
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\textit{Merlins Steine} wurde ausgewählt, da Sets von anderen Marken im Vergleich zu vorherig genannten Quellen aufgelistet werden. Der Datensatz enthält Informationen zum \ac{UVP}, Teileanzahl, Release-Jahr, Thema Modelldimensionen und des Herstellers.
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\begin{table}[H]
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\begin{tabularx}{\columnwidth}{@{}ll@{}}
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