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Add SPARQL custom endpoint
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parent
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459
lod/02_SPARQL_Custom_Endpoint.ipynb
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459
lod/02_SPARQL_Custom_Endpoint.ipynb
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"checksum": "7276f055a8c504d3c80098c62ed41a4f",
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"grade": false,
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"grade_id": "cell-0bfe38f97f6ab2d2",
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"locked": true,
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"schema_version": 1,
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"solution": false
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}
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},
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"source": [
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"<header style=\"width:100%;position:relative\">\n",
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" <div style=\"width:80%;float:right;\">\n",
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" <h1>Course Notes for Learning Intelligent Systems</h1>\n",
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" <h3>Department of Telematic Engineering Systems</h3>\n",
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" <h5>Universidad Politécnica de Madrid</h5>\n",
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" </div>\n",
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" <img style=\"width:15%;\" src=\"../logo.jpg\" alt=\"UPM\" />\n",
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"</header>"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"checksum": "42642609861283bc33914d16750b7efa",
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"grade": false,
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"grade_id": "cell-0cd673883ee592d1",
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"locked": true,
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"schema_version": 1,
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"solution": false
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}
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},
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"source": [
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"## Introduction\n",
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"\n",
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"In the previous notebook, we learnt how to use SPARQL by querying DBpedia.\n",
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"\n",
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"In this notebook, we will use SPARQL on manually annotated data. The data was collected as part of a [previous exercise](../lod/).\n",
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"\n",
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"The goal is to try SPARQL with data annotated by users with limited knowledge of vocabularies and semantics, and to compare the experience with similar queries to a more structured dataset.\n",
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"\n",
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"Hence, there are two parts.\n",
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"First, you will query a set of graphs annotated by students of this course.\n",
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"Then, you will query a synthetic dataset that contains similar information."
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"checksum": "a3ecb4b300a5ab82376a4a8cb01f7e6b",
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"grade": false,
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"grade_id": "cell-10264483046abcc4",
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"locked": true,
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"schema_version": 1,
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"solution": false
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}
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},
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"source": [
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"## Objectives\n",
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"\n",
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"* Experiencing the usefulness of the Linked Open Data initiative by querying data from different RDF graphs and endpoints\n",
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"* Understanding the challenges in querying multiple sources, with different annotators.\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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"checksum": "2fedf0d73fc90104d1ab72c3413dfc83",
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"grade": false,
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"grade_id": "cell-4f8492996e74bf20",
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"locked": true,
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"schema_version": 1,
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"solution": false
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}
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},
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"source": [
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"## Tools\n",
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"\n",
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"See [the SPARQL notebook](./01_SPARQL_Introduction.ipynb#Tools)"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"checksum": "c5f8646518bd832a47d71f9d3218237a",
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"grade": false,
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"grade_id": "cell-eb13908482825e42",
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"locked": true,
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"schema_version": 1,
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"solution": false
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}
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},
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"source": [
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"Run this line to enable the `%%sparql` magic command."
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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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"from helpers import *"
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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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"## Exercises\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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"Querying the manually annotated dataset will be slightly different from querying DBpedia.\n",
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"The main difference is that this dataset uses different graphs to separate the annotations from different students.\n",
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"\n",
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"**Each graph is a separate set of triples**.\n",
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"For this exercise, you could think of graphs as individual endpoints.\n",
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"\n",
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"\n",
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"First, let us get a list of graphs available:"
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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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"%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
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" \n",
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"SELECT ?g (COUNT(?s) as ?count) WHERE {\n",
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" GRAPH ?g {\n",
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" ?s ?p ?o\n",
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" }\n",
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"}\n",
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"GROUP BY ?g\n",
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"ORDER BY desc(?count)"
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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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"You should see many graphs, with different triple counts.\n",
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"\n",
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"The biggest one should be http://fuseki.cluster.gsi.dit.upm.es/synthetic"
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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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"Once you have this list, you can query specific graphs like so:"
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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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"%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
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" \n",
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"SELECT *\n",
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"WHERE {\n",
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" GRAPH <http://fuseki.cluster.gsi.dit.upm.es/synthetic>{\n",
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" ?s ?p ?o .\n",
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" }\n",
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"}\n",
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"LIMIT 10"
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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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"There are two exercises in this notebook.\n",
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"\n",
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"In each of them, you are asked to run five queries, to answer the following questions:\n",
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"\n",
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"* Number of hotels (or entities) with reviews\n",
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"* Number of reviews\n",
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"* The hotel with the lowest average score\n",
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"* The hotel with the highest average score\n",
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"* A list of hotels with their addresses and telephone numbers"
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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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"### Manually annotated data"
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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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"Your task is to design five queries to answer the questions in the description, and run each of them in at least three graphs, other than the `synthetic` graph.\n",
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"\n",
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"To design the queries, you can either use what you know about the schema.org vocabularies, or explore subjects, predicates and objects in each of the graphs.\n",
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"\n",
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"You will get a better understanding if you follow the exploratory path.\n",
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"\n",
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"Here's an example to get the entities and their types in a 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
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"\n",
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"PREFIX schema: <http://schema.org/>\n",
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" \n",
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"SELECT ?s ?o\n",
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"WHERE {\n",
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" GRAPH <http://fuseki.cluster.gsi.dit.upm.es/35c20a49f8c6581be1cf7bd56d12d131>{\n",
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" ?s a ?o .\n",
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" }\n",
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"\n",
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"}\n",
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"LIMIT 40"
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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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"### Synthetic dataset\n",
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"\n",
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"Now, run the same queries in the synthetic dataset.\n",
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"\n",
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"The query below should get you started:"
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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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"%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
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" \n",
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"SELECT *\n",
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"WHERE {\n",
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" GRAPH <http://fuseki.cluster.gsi.dit.upm.es/synthetic>{\n",
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" ?s ?p ?o .\n",
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" }\n",
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"}\n",
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"LIMIT 10"
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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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"### Optional exercise\n",
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"\n",
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"\n",
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"Explore the graphs and find the most typical mistakes (e.g. using `http://schema.org/Hotel/Hotel`).\n",
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"\n",
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"Tip: You can use normal SPARQL queries with `BOUND` and `REGEX` to check if the annotations are correct.\n",
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"\n",
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"You can also query all the graphs at the same time. e.g. to get all types used:"
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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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"%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
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"\n",
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"PREFIX schema: <http://schema.org/>\n",
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" \n",
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"SELECT DISTINCT ?o\n",
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"WHERE {\n",
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" GRAPH ?g {\n",
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" ?s a ?o .\n",
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" }\n",
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" {\n",
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" SELECT ?g\n",
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" WHERE {\n",
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" GRAPH ?g {}\n",
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" FILTER (str(?g) != 'http://fuseki.cluster.gsi.dit.upm.es/synthetic')\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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"LIMIT 50"
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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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"### Discussion\n",
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"\n",
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"Compare the results of the synthetic and the manual dataset, and answer these questions:"
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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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"Both datasets should use the same schema. Are there any differences when it comes to using them?"
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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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"deletable": false,
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"nbgrader": {
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"checksum": "860c3977cd06736f1342d535944dbb63",
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"grade": true,
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"grade_id": "cell-9bd08e4f5842cb89",
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"locked": false,
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"points": 0,
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"schema_version": 1,
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"solution": true
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}
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},
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"outputs": [],
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"source": [
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"# YOUR ANSWER HERE"
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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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"Are the annotations used correctly in every 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": null,
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"metadata": {
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"deletable": false,
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"nbgrader": {
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"checksum": "1946a7ed4aba8d168bb3fad898c05651",
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"grade": true,
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"grade_id": "cell-9dc1c9033198bb18",
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"locked": false,
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"points": 0,
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"schema_version": 1,
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"solution": true
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}
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},
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"outputs": [],
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"source": [
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"# YOUR ANSWER HERE"
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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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"Has any of the datasets been harder to query? If so, why?"
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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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||||||
|
"deletable": false,
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|
"nbgrader": {
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||||||
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"checksum": "6714abc5226618b76dc4c1aaed6d1a49",
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"grade": true,
|
||||||
|
"grade_id": "cell-6c18003ced54be23",
|
||||||
|
"locked": false,
|
||||||
|
"points": 0,
|
||||||
|
"schema_version": 1,
|
||||||
|
"solution": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# YOUR ANSWER HERE"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## References"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"* [RDFLib documentation](https://rdflib.readthedocs.io/en/stable/).\n",
|
||||||
|
"* [Wikidata Query Service query examples](https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Licence\n",
|
||||||
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
||||||
|
"\n",
|
||||||
|
"© 2018 Universidad Politécnica de Madrid."
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"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": "3.7.2"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
Loading…
Reference in New Issue
Block a user