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Add example SPARQL queries
Some example SPARQL queries were added that represent querying of relevant information typically obtained from tensile tests. Additionally, a Jupyter notebook is provided to run the queries.
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‎tensile_test_data/S355_SPARQL_query.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# S355 Tensile Test SPARQL Queries\n",
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"\n",
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"This Jupyter Notebook provides some examples of SPARQL queries that can be performed to obtain information relevant to tensile testing. \n",
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"An [example dataset of tensile tests performed on an S355 steel](https://github.com/materialdigital/tensile-test-ontology/blob/main/tensile_test_data/S355_data_tto.rdf) is used as a basis. In this Jupyter Notebook, a local triple store is created using the OWLready2 Python package. Within this triple store, the respective ontology and the data are loaded and can be queried afterwards.\n",
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"Accordingly, necessary and useful libraries are imported and helper functions are implemented.\n",
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"The SPARQL queries are read in from especially created files that contain only the SPARQL query body (text of SPARQL query) and can be found in a dedicated [sparql folder](https://github.com/materialdigital/tensile-test-ontology/tree/main/tensile_test_data/sparql).\n",
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"\n",
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"The queries follow the general pattern of SPARQL queries:\n",
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"\n",
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"```SPARQL\n",
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"PREFIX ex: <https://example.org/my/namespace/>\n",
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"\n",
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"SELECT ?s ?p ?o\n",
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"WHERE {\n",
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" ?s ?p ?o\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": "markdown",
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"metadata": {},
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"source": [
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"## Import of relevant packages | Definition of helper functions"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"# Import relevant and useful packages\n",
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"import requests\n",
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"from io import BytesIO\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import owlready2 as or2\n",
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"from owlready2 import World\n",
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"import re\n",
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"from tabulate import tabulate\n",
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"\n",
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"# Definition of helper functions\n",
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"# Function to transform inputs to IRIs.\n",
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"def to_iri(input):\n",
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" try:\n",
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" return input.iri\n",
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" except:\n",
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" pass\n",
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" return input\n",
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"\n",
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"# Function to write the result of a SPARQL query into a (pandas) data frame.\n",
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"def sparql_result_to_df(res):\n",
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" l = []\n",
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" for row in res:\n",
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" r = [ to_iri(item) for item in row]\n",
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" l.append(r)\n",
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" return pd.DataFrame(l)\n",
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"\n",
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"\n",
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"def load_ontologies_to_world(*ontology_urls):\n",
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" \"\"\"\n",
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" Loads ontologies from the given URLs into an OWLready2 World instance.\n",
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" \n",
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" Parameters:\n",
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" ontology_urls: A variable number of URLs pointing to ontologies.\n",
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" \n",
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" Returns:\n",
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" An OWLready2 World instance containing the loaded ontologies.\n",
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" \"\"\"\n",
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" # Create a new World instance for loading ontologies\n",
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" world = World()\n",
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" \n",
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" # Iterate over each provided ontology URL\n",
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" for url in ontology_urls:\n",
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" try:\n",
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" # Fetch the ontology content, following redirects\n",
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" response = requests.get(url, allow_redirects=True)\n",
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" response.raise_for_status() # Check for HTTP errors\n",
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"\n",
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" # Load the ontology from the response content\n",
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" world.get_ontology(url).load(fileobj=BytesIO(response.content))\n",
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" \n",
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" except requests.exceptions.RequestException as e:\n",
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" print(f\"Failed to load ontology from {url}: {e}\")\n",
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" \n",
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" return world\n",
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"\n",
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"\n",
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"import requests\n",
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"\n",
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"def load_sparql(query_name: str) -> str:\n",
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" \"\"\"\n",
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" Loads a SPARQL query file directly from GitHub (raw URL).\n",
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" \n",
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" Parameters\n",
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" ---------\n",
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" query_name : str\n",
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" Name of the SPARQL file without extension (.sparql).\n",
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" \n",
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" Return\n",
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" --------\n",
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" str\n",
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" Content of the SPARQL file as a string.\n",
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" \"\"\"\n",
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" base_url = \"https://raw.githubusercontent.com/materialdigital/tensile-test-ontology/main/tensile_test_data/sparql\"\n",
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" url = f\"{base_url}/{query_name}.sparql\"\n",
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" \n",
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" response = requests.get(url)\n",
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" if response.status_code == 200:\n",
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" return response.text\n",
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" else:\n",
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" raise FileNotFoundError(f\"Datei konnte nicht geladen werden: {url} (Status {response.status_code})\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Definition of Sources\n",
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"\n",
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"In the following cell, the sources of ontologies to be parsed as well as the source of the A-Box (example dataset of tensile tests performed on an S355 steel) are specified."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Definition of links to ontologies, files, etc. to be loaded in the local triple store\n",
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"link_ontology_1 = \"https://w3id.org/pmd/co/\" # PMD Core Ontology (PMDco) as basis for tensile test ontology\n",
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"link_ontology_2 = \"https://w3id.org/pmd/ao/tto/\" # Tensile Test Ontology (TTO)\n",
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"link_data = \"https://raw.githubusercontent.com/materialdigital/tensile-test-ontology/refs/heads/main/tensile_test_data/S355_data_tto.rdf\" # Example data on S355 steel\n",
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"\n",
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"# Loading ontologies and data files (A-Box) in the local triple store\n",
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"triple_store = load_ontologies_to_world(link_ontology_1, link_ontology_2)\n",
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"triple_store.get_ontology(link_data).load()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SPARQL Query\n",
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"\n",
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"In the following cell, the source, meaning the name, of the SPARQL query file **is to be selected / specified by users**. \n",
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"\n",
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"The query contained in this file will be used for querying in the subsequent cell.\n",
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"\n",
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"### Depiction of Results \n",
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"\n",
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"For a depiction / visualization of results in table format, the module tabulate is used in the following.\n",
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"Furthermore, as the SPARQL query is defined by a dedicated SPARQL query file (link_SPARQL_query), the headers of the result table can be read from the select clause in the query. This way, the result can be double-checked manually and consistency is ensured (did the SPARQL query select statement really address the information I wanted to obtain?). Hence, the following code includes a read in of the information queried for (the terms / concepts / entities addressed using the select clause)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Specification of the SPARQL query of interest\n",
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"# Which SPARQL query is to be performed?\n",
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"# Please insert the name of the query (to be found in the \"sparql\" folder)\n",
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"\n",
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"query_name = 'count_all_entities'"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the file from the resource and read the SPARQL query\n",
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"query = load_sparql(query_name)\n",
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"\n",
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"# Execute the SPARQL query\n",
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"res = triple_store.sparql(query)\n",
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"\n",
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"# Convert the result to a DataFrame\n",
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"data = sparql_result_to_df(res)\n",
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"\n",
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"# Visualization Part\n",
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"# Step: Extract the terms from the SELECT clause\n",
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"# This regular expression looks for the SELECT or SELECT DISTINCT clause and captures the terms.\n",
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"select_clause_match = re.search(r'SELECT\\s+(DISTINCT\\s+)?(.*?)\\s+WHERE', query, re.DOTALL)\n",
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"\n",
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"if select_clause_match:\n",
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" select_clause = select_clause_match.group(2) # Use group(2) to capture the variables\n",
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" # Split the terms by whitespace and strip any leading or trailing spaces\n",
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" headers = [term.strip().lstrip('?') for term in select_clause.split() if term.strip().startswith('?')]\n",
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"else:\n",
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" print(\"No headers were found. Please check the select clause within the SPARQL query.\")\n",
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"\n",
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"# Step: Use the headers in the tabulate print statement\n",
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"# Print the data with tabulate\n",
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"print(tabulate(data, headers=headers, tablefmt='psql', showindex=True))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Perform all SPARQL Queries\n",
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"\n",
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"Using the following cell, all SPARQL queries available in the [example sparql folder]() will be performed one after the other automatically. All results are depicted. "
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# GitHub API URL to list all files in the SPARQL folder\n",
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"repo_api_url = \"https://api.github.com/repos/materialdigital/tensile-test-ontology/contents/tensile_test_data/sparql\"\n",
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"\n",
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"# Get the JSON response\n",
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"response = requests.get(repo_api_url)\n",
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"files_json = response.json()\n",
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"\n",
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"# Filter only .sparql files\n",
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"query_files = [f['name'] for f in files_json if f['name'].endswith('.sparql')]\n",
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"\n",
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"# Dictionary to store results\n",
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"all_results = {}\n",
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"\n",
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"for query_file in query_files:\n",
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" query_name = query_file.replace(\".sparql\", \"\")\n",
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" try:\n",
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" # Load SPARQL content from GitHub using your existing function\n",
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" query = load_sparql(query_name)\n",
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" \n",
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" # Execute SPARQL query on the triple store\n",
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" res = triple_store.sparql(query)\n",
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" \n",
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" # Convert to DataFrame\n",
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" df = sparql_result_to_df(res)\n",
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" \n",
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" # Store in dictionary\n",
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" all_results[query_name] = df\n",
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" \n",
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" # Extract headers from SELECT clause\n",
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" select_clause_match = re.search(r'SELECT\\s+(DISTINCT\\s+)?(.*?)\\s+WHERE', query, re.DOTALL)\n",
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" if select_clause_match:\n",
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" select_clause = select_clause_match.group(2)\n",
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" headers = [term.strip().lstrip('?') for term in select_clause.split() if term.strip().startswith('?')]\n",
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" else:\n",
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" headers = None\n",
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" \n",
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" # Print results nicely\n",
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" print(f\"\\n=== Results for Query: {query_name} ===\")\n",
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" print(tabulate(df, headers=headers, tablefmt='psql', showindex=True))\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"Error executing query '{query_name}': {e}\")"
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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": "Python 3",
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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": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tensile_test_data/sparql/count_all_entities.sparql‎

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SELECT (COUNT(?s) AS ?count)
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WHERE {
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?s ?p ?o
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}

‎tensile_test_data/sparql/count_number_of_tensile_test.sparql‎

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PREFIX pmdco: <https://w3id.org/pmd/co/>
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SELECT (COUNT(?process) AS ?numerOfTests)
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WHERE {
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?process a pmdco:PMD_0000974 .
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FILTER(CONTAINS(STR(?process), "_process"))
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}

‎tensile_test_data/sparql/find_materials_tested.sparql‎

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PREFIX pmdco: <https://w3id.org/pmd/co/>
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PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
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PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
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SELECT DISTINCT ?material_tested
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WHERE {
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# Find the instance describing the material identification
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?identifier pmdco:PMD_0060000 ?material_value .
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FILTER(CONTAINS(STR(?material_value), "_material_identifier_value"))
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# Get the actual value (literal), regardless of whether it is rdfs:label or rdf:value or similar.
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?material_value ?p ?material_tested .
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FILTER(isLiteral(?material_tested))
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}

‎tensile_test_data/sparql/list_all_triples.sparql‎

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SELECT ?s ?p ?o
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WHERE {
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?s ?p ?o
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}

‎tensile_test_data/sparql/name_subprocesses_ordered.sparql‎

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PREFIX obo: <http://purl.obolibrary.org/obo/>
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PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
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PREFIX pmdco: <https://w3id.org/pmd/co/>
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PREFIX prefix: <https://w3id.org/pmd/ao/demodata/tensiletest_S355/>
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SELECT ?subprocessShort
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WHERE {
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{
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SELECT ?mainProcess
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WHERE {
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?mainProcess a pmdco:PMD_0000974 .
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FILTER(CONTAINS(STR(?mainProcess), "_process"))
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}
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LIMIT 1
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}
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?subprocess obo:BFO_0000132 ?mainProcess .
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OPTIONAL { ?subprocess (obo:BFO_0000062)+ ?prev }
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# Abkürzen auf "prefix:..."
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BIND(REPLACE(STR(?subprocess), "^https://w3id.org/pmd/ao/demodata/tensiletest_S355/pmdao-tto-tt-S355-1_process_", "") AS ?subprocessShort)
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}
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GROUP BY ?subprocessShort
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ORDER BY (COUNT(?prev))

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