diff --git a/lod/00_SPARQL_Tutorial.ipynb b/lod/00_SPARQL_Tutorial.ipynb new file mode 100644 index 0000000..e81e773 --- /dev/null +++ b/lod/00_SPARQL_Tutorial.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](files/images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "

Course Notes for Learning Intelligent Systems

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Department of Telematic Engineering Systems

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Universidad Politécnica de Madrid. © Carlos A. Iglesias
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\n", + " \"UPM\"\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This lecture provides an introduction to RDF and the query language SPARQL.\n", + "\n", + "This is the first in a series of notebooks about SPARQL, which consists of:\n", + "\n", + "* This notebook, which basic concepts of RDF and SPARQL\n", + "* [A notebook](01_SPARQL_Introduction.ipynb) that provides an introduction of SPARQL through a collection of progressively more difficult exercises]\n", + "* [A notebook](02_SPARQL_Custom_Endpoint.ipynb) with queries to a custom dataset, which links to the RDF exercises and it is out of the scope of this course. You can consult it if you are interested." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RDF basics\n", + "This section is taken from [[1](#1), [2](#2)].\n", + "\n", + "RDF allows us to make statements about resources. The format of these statements is simple. A statement always has the following structure:\n", + "\n", + " \n", + " \n", + "An RDF statement expresses a relationship between two resources. The **subject** and the **object** represent the two resources being related; the **predicate** represents the nature of their relationship. The relationship is phrased in a directional way (from subject to object) and is called in RDF a **property**. Because RDF statements consist of three elements they are called **triples**.\n", + "\n", + "Here are examples of RDF triples (informally expressed in pseudocode):\n", + "\n", + " .\n", + " .\n", + " \n", + "Resources are identified by IRIs, which can appear in all three positions of a triple. For example, the IRI for Leonardo da Vinci in DBpedia is:\n", + "\n", + " \n", + "\n", + "IRIs can be abbreviated as *prefixed names*. For example, \n", + " PREFIX dbr: \n", + " \n", + " \n", + "Objects can be literals: \n", + " * strings (e.g., \"plain string\" or \"string with language\"@en)\n", + " * numbers (e.g., \"13.4\"^^xsd:float)\n", + " * dates (e.g., )\n", + " * booleans\n", + " * etc.\n", + " \n", + "RDF data is stored in RDF repositories that expose SPARQL endpoints. Let's query one of the most famous RDF repositories: dbpedia. First, we should learn how to execute SPARQL in a notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Executing SPARQL in a notebook\n", + "There are several ways to execute SPARQL in a notebook. The most popular ones are:\n", + "* using a SPARQL kernel [sparql kernel](https://github.com/paulovn/sparql-kernel) instead of the Python3 kernel\n", + "* using the [graph notebook package](https://pypi.org/project/graph-notebook/)\n", + "* using libraries such as [sparql-client](https://pypi.org/project/sparql-client/) or [rdflib](https://rdflib.dev/sparqlwrapper/) that enable executing SPARQL within a Python3 kernel\n", + "* using other libraries. In our case, a light library has been developed (the file helpers.py) for accessing SPARQL endpoints using an HTTP connection.\n", + "\n", + "We are going to use the last option to avoid installing new packages.\n", + "\n", + "For using the library, you need:\n", + "1. Import sparql from helpers (the file helpers.py available in the github repository)\n", + "2. Use the magic command '%%sparql' to indicate the SPARQL endpoint and then the SPARQL code.\n", + "\n", + "Let's try it!\n", + "\n", + "# Queries agains DBPedia\n", + "\n", + "We are going to execute an SPARQL query agains DBPedia. This section is based on [[8](#8)].\n", + "\n", + "First, we just create a query to retrieve arbitrary triples (subject, predicate, object) without any restriction (only that we want to limit to 10 results)." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from helpers import sparql" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
spo
http://www.openlinksw.com/virtrdf-data-formats#default-iidhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#default-iid-nullablehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#default-iid-nonblankhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#default-iid-nonblank-nullablehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#defaulthttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#default-nullablehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#sql-varcharhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#sql-varchar-nullablehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#sql-varchar-dthttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
http://www.openlinksw.com/virtrdf-data-formats#sql-varchar-dt-nullablehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.openlinksw.com/schemas/virtrdf#QuadMapFormat
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://live.dbpedia.org/sparql\n", + "\n", + "SELECT ?s ?p ?o\n", + "WHERE {\n", + " ?s ?p ?o\n", + "}\n", + "LIMIT 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, it worked, but the results are not particulary interesting. \n", + "Let's search for a famous football player, Fernando Torres." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
athlete
http://dbpedia.org/resource/Fernando_Torres
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://live.dbpedia.org/sparql\n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?athlete rdfs:label \"Fernando Torres\"@en \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Amazing. Go to http://dbpedia.org/resource/Fernando_Torres and you will see all the information available about Fernando Torres. Pay attention to the names of predicates to be able to create new queries. For example, we are interesting in knowing where Fernando Torres was born.\n", + "\n", + "Let's go!" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
athletebirthPlace
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Spain_national_football_team
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabrada
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?athlete rdfs:label \"Fernando Torres\"@en ;\n", + " dbo:birthPlace ?birthPlace . \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe the SPARQL query:\n", + "* PREFIX section URIs of vocabularies and the prefix used below, to avoid long IRIs\n", + "* SELECT section: variables we want to return (* is an abbreviation that selects all of the variables in a query)\n", + "* WHERE triple pattern: triples where some elements are variables. These variables are bound during the query processing process and bounded variables are returned.\n", + "\n", + "Pay attention to the WHERE section. Since both triple patterns share the same subject, we omit it in the second one, and link both with \" ;\". Each triple pattern should finish with a \" .\" (the last pattern can omit this). Don't forget the space before \";\" and \".\".\n", + "\n", + "The result is interesting, we know he was born in Fuenlabrada, but we see an additional (wrong) value, the Spanish national football team. The conversion process from Wikipedia to DBPedia should still be tuned :).\n", + "\n", + "We can 'fix' it, by adding some information. We want only municipalities as a result. Let's see!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
athletebirthPlace
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabrada
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbr: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?athlete rdfs:label \"Fernando Torres\"@en ;\n", + " dbo:birthPlace ?birthPlace .\n", + " ?birthPlace dbo:type dbr:Municipalities_of_Spain \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great. Now it looks better.\n", + "Do you know of Fuenlabrada is a big city? Let's query!\n", + "\n", + "Hint: search (as previously) the subject / object / predicate nodes in the RDF graph (http://dbpedia.org/resource/Fuenlabrada).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
area
3.941e+07
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbr: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " dbr:Fuenlabrada dbo:areaTotal ?area \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, it shows 39.1 km$^2$. Let's go back to know more about Fernando Torres. We would want to retrieve the name of the city where he was born instead of the IRI. Let's try!" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
playerbirthPlaceplaceName
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabradaفوينلابرادا
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabradaフエンラブラダ
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabrada푸엔라브라다
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaФуэнлабрада
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaФуенлабрада
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/Fuenlabrada富恩拉夫拉达
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbp: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?player rdfs:label \"Fernando Torres\"@en ;\n", + " dbo:birthPlace ?birthPlace .\n", + " ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n", + " rdfs:label ?placeName \n", + " \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, we are almost there. We see that we receive the city name in many languages. We want just the English name. Let's filter!" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
playerbirthPlaceplaceName
http://dbpedia.org/resource/Fernando_Torreshttp://dbpedia.org/resource/FuenlabradaFuenlabrada
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbp: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?player rdfs:label \"Fernando Torres\"@en ;\n", + " dbo:birthPlace ?birthPlace .\n", + " ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n", + " rdfs:label ?placeName .\n", + " FILTER ( LANG ( ?placeName ) = 'en' )\n", + " \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome. Let's tune a bit more. We only want two results: Fernando's birth date and birth place (name). Let's go!" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
birthDateplaceName
1984-03-20Fuenlabrada
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbp: \n", + "\n", + "SELECT ?birthDate, ?placeName\n", + "WHERE\n", + " {\n", + " ?player rdfs:label \"Fernando Torres\"@en ;\n", + " dbo:birthDate ?birthDate ;\n", + " dbo:birthPlace ?birthPlace .\n", + " ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n", + " rdfs:label ?placeName .\n", + " FILTER ( LANG ( ?placeName ) = 'en' )\n", + " \n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Great :). Are there many football players born in Fuenlabrada? Let's query!" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
player
http://dbpedia.org/resource/Luismi_(footballer,_born_1979)
http://dbpedia.org/resource/Óscar_Miñambres
http://dbpedia.org/resource/Tachi_(footballer)
http://dbpedia.org/resource/Fernando_Torres
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sparql https://dbpedia.org/sparql\n", + "\n", + "PREFIX dbo: \n", + "PREFIX dbp: \n", + "\n", + "SELECT *\n", + "WHERE\n", + " {\n", + " ?player a dbo:SoccerPlayer ; \n", + " dbo:birthPlace dbr:Fuenlabrada .\n", + " \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well, not that many. Observe we have used 'a' (it is an abbreviation for rdf:type, both can be used).\n", + "\n", + "If you want additional examples, you can follow the notebook by [Shawn Graham](https://github.com/o-date/sparql-and-lod/blob/master/sparql-intro.ipynb), which is based on the SPARQL tutorial by Matthew Lincoln, available [here in English](https://programminghistorian.org/en/lessons/retired/graph-databases-and-SPARQL) and [here in Spanish](https://programminghistorian.org/es/lecciones/retirada/sparql-datos-abiertos-enlazados]). You have also a local copy of these tutorials together with this notebook [here in English](https://htmlpreview.github.io/?https://github.com/gsi-upm/sitc/blob/master/lod/tutorial/graph-databases-and-SPARQL.html) and [here in Spanish](https://htmlpreview.github.io/?https://github.com/gsi-upm/sitc/blob/master/lod/tutorial/sparql-datos-abiertos-enlazados.html). \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* [1] [SPARQL by Example. A Tutorial. Lee Feigenbaum. W3C, 2009](https://www.w3.org/2009/Talks/0615-qbe/#q1)\n", + "* [2] [RDF Primer W3C](https://www.w3.org/TR/rdf11-primer/)\n", + "* [3] [SPARQL queries of Beatles recording sessions](http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html)\n", + "* [4] [RDFLib documentation](https://rdflib.readthedocs.io/en/stable/).\n", + "* [5] [Wikidata Query Service query examples](https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples)\n", + "* [6] [RDF Graph Data Model. Learn about the RDF graph model used by Stardog.](https://www.stardog.com/tutorials/data-model)\n", + "* [7] [Learn SPARQL Write Knowledge Graph queries using SPARQL with step-by-step examples.](https://www.stardog.com/tutorials/sparql/)\n", + "* [8] [Running Basic SPARQL Queries Against DBpedia.](https://medium.com/virtuoso-blog/dbpedia-basic-queries-bc1ac172cc09)\n", + "* [9] [Intro SPARQL based on painters.](https://github.com/o-date/sparql-and-lod/blob/master/sparql-intro.ipynb)." + ] + }, + { + "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", + "© Carlos A. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, + "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": "3.7.9" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/lod/tutorial/sparql-intro.ipynb b/lod/tutorial/sparql-intro.ipynb new file mode 100644 index 0000000..ae5a904 --- /dev/null +++ b/lod/tutorial/sparql-intro.ipynb @@ -0,0 +1,4112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to SPARQL, RDF, and LOD\n", + "\n", + "While many databases, services, or museums might expose their data via a web API, there can be limitations. Matthew Lincoln has an excellent tutorial at [The Programming Historian](https://programminghistorian.org/en/lessons/graph-databases-and-SPARQL) that walks us through some of these differences, but the key one is in the way the data is represented. When data is described using a 'Resource Description Framework', RDF, the resource - the 'thing'- is described via a series of relationships, rather than as rows in a table or keys having values. \n", + "\n", + "Information is in the relationships. It's a network. It's a _graph_. Thus, every 'thing' in this graph can have its own _uniform resource identifier_ (URI) that lives as a location on the internet. Information can then be created by making _statements_ that use these URIs, similarly to how English grammar creates meaning: subject verb object. Or, in RDF-speak, 'subject predicate object', also known as a _triple_. In this way, data in _different_ places can be linked together by referencing the elements they have in common. This is Linked Open Data (LOD). The access point for interrogating LOD is called an 'endpoint'. \n", + "\n", + "Finally, _SPARQL_ is an acronymn for SPARQL Protocol and RDF Query Language (yes, it's one of _those_ kinds of acronyms).\n", + "\n", + "In this notebook, we're not using Python or R directly. Instead, we've set up a 'kernel' (think of that as the 'engine' for the notebook) that already includes everything necessary to set up and run SPARQL queries. (For reference, the kernel code is [here](https://github.com/paulovn/sparql-kernel)). Both R and Python can interact with and query endpoints, and manipulate linked open data, but for the sake of learning a bit of what one can do with SPARQL, this notebook keeps all of that ancillary code tucked away. The [followup notebook](Using R to Retrieve and Visualize Data from SPARQL.ipynb) to this one shows you how to use R to do some basic manipulations of the query results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Simple RDF example\n", + "\n", + "Here, we are following [Matthew Lincoln's tutorial](https://programminghistorian.org/en/lessons/graph-databases-and-SPARQL). \n", + "\n", + "Let's look at his example, which concerns the painting, 'The Nightwatch'. \n", + "\n", + "` .`\n", + "\n", + "This _statement_ has three elements: \n", + "\n", + "+ the subject: ``\n", + "+ the predicate: ``\n", + "+ the object: ``\n", + "\n", + "Lincoln combines these, and other such statements, into a (pseudo-)RDF database like so:\n", + "\n", + "```\n", + " .\n", + " <1642> .\n", + " .\n", + " <1606> .\n", + " .\n", + " .\n", + " .\n", + " .\n", + "```\n", + "\n", + "Such RDF databases are describing nodes and links, and so we can visualize as a graph like so:\n", + "\n", + "![A network visualization of the pseudo-RDF shown above. Arrows indicate the ‘direction’ of the predicate. For example, that ‘Woman with a Balance was created by Vermeer’, and not the other way around.](https://programminghistorian.org/images/graph-databases-and-SPARQL/sparql01.svg)\n", + "\n", + "But there is a difference between the pseudo-RDF that Lincoln shows us, and what _actual_ RDF might look like:\n", + "\n", + "```\n", + " \n", + "```\n", + "\n", + "The human-readable version requires _more_ statements:\n", + "\n", + "```\n", + " \"The Nightwatch\" .\n", + "\n", + " \"was created by\" .\n", + "\n", + " \"Rembrandt van Rijn\" .\n", + "```\n", + "\n", + "This is just a quick introduction; please do examine [Lincoln's tutorial](https://programminghistorian.org/en/lessons/graph-databases-and-SPARQL) for more details. But now, let's explore how this notebook can be used to write some queries.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Available magics:
%auth %display %endpoint %format %graph %lang %log %lsmagics %outfile %prefix %qparam %show

%auth (basic|digest|none) <username> <passwd> : send HTTP authentication
%display raw | table [withtypes] | diagram [svg|png] [withliterals] : set display format
%endpoint <url> : set SPARQL endpoint. **REQUIRED**
%format JSON | N3 | XML | any | default : set requested result format
%graph <uri> : set default graph for the queries
%lang <lang> [...] | default | all : language(s) preferred for labels
%log critical | error | warning | info | debug : set logging level
%lsmagics : list all magics
%outfile <filename> | NONE : save raw output to a file (use "%d" in name to add cell number, "NONE" to cancel saving)
%prefix <name> [<uri>] : set (or delete) a persistent URI prefix for all queries
%qparam <name> [<value>] : add (or delete) a persistent custom parameter to the endpoint query
%show <n> | all : maximum number of shown results
" + ], + "text/plain": [ + "Available magics:\n", + "%auth %display %endpoint %format %graph %lang %log %lsmagics %outfile %prefix %qparam %show\n", + "\n", + "%auth (basic|digest|none) : send HTTP authentication\n", + "%display raw | table [withtypes] | diagram [svg|png] [withliterals] : set display format\n", + "%endpoint : set SPARQL endpoint. **REQUIRED**\n", + "%format JSON | N3 | XML | any | default : set requested result format\n", + "%graph : set default graph for the queries\n", + "%lang [...] | default | all : language(s) preferred for labels\n", + "%log critical | error | warning | info | debug : set logging level\n", + "%lsmagics : list all magics\n", + "%outfile | NONE : save raw output to a file (use \"%d\" in name to add cell number, \"NONE\" to cancel saving)\n", + "%prefix [] : set (or delete) a persistent URI prefix for all queries\n", + "%qparam [] : add (or delete) a persistent custom parameter to the endpoint query\n", + "%show | all : maximum number of shown results\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Jupyter notebooks have various built-in commands called 'magics' that are accessed with the '%' character; these depend on the kernel. \n", + "# Let's see what the SPARQL kernel has\n", + "%lsmagics" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://collection.britishmuseum.org/sparql
" + ], + "text/plain": [ + "Endpoint set to: http://collection.britishmuseum.org/sparql\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# when using this notebook, the first thing we have to do - or rather, the first time we run _any_ query,\n", + "# is to tell it what endpoint we're going to use. Let's use the British Museum's:\n", + "\n", + "%endpoint http://collection.britishmuseum.org/sparql" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lincoln suggests that when we first encountered a new RDF graph, that we explore the network of relationships from an example object to understand what is going on in the database, to see what is available for querying. Since we're querying the British Museum, let's take [the Rosetta Stone](http://collection.britishmuseum.org/id/object/YCA62958) as our example.\n", + "\n", + "In the query below, `p` and `o` stand for 'predicate' and 'object'. Thus, we're building up a query that asks, 'show me every statment structured ` `. When the results load up, you can right-click on each statement (which is a URI, remember) to see what we've discovered. This could give you the necessary information to construct more complicated queries.\n", + "\n", + "*Nb* The British Museum sparql endpoint and the underlying infrastructure does not appear to be well supported. Results are sometimes flaky or not reachable." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ptypeotype
http://www.cidoc-crm.org/cidoc-crm/P138i_has_representationurihttps://images.researchspace.org/collectionimages/AN00020/AN00020516_001_l.jpguri
http://www.cidoc-crm.org/cidoc-crm/P138i_has_representationurihttps://images.researchspace.org/collectionimages/AN00773/AN00773236_001_l.jpguri
http://www.cidoc-crm.org/cidoc-crm/P138i_has_representationurihttps://images.researchspace.org/collectionimages/AN00773/AN00773255_001_l.jpguri
http://www.cidoc-crm.org/cidoc-crm/P138i_has_representationurihttps://images.researchspace.org/collectionimages/AN00871/AN00871967_001_l.jpguri
http://www.cidoc-crm.org/cidoc-crm/P138i_has_representationurihttps://images.researchspace.org/collectionimages/AN00928/AN00928245_001_l.jpguri
http://www.researchspace.org/ontology/PX_has_main_representationurihttps://images.researchspace.org/collectionimages/AN00016/AN00016456_004_l.jpguri
http://www.researchspace.org/ontology/Thing_created_on_Timeurihttp://collection.britishmuseum.org/id/object/YCA62958/production/1/dateuri
http://www.researchspace.org/ontology/Thing_has_material_type_Concepturihttp://collection.britishmuseum.org/id/thesauri/x10901uri
http://www.researchspace.org/ontology/Thing_has_material_type_Concepturihttp://collection.britishmuseum.org/id/thesauri/x11794uri
http://www.researchspace.org/ontology/Thing_has_material_type_Concepturihttp://collection.britishmuseum.org/id/thesauri/x11014uri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E1_CRM_Entityuri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E18_Physical_Thinguri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E19_Physical_Objecturi
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E24_Physical_Man-Made_Thinguri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E70_Thinguri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E71_Man-Made_Thinguri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E72_Legal_Objecturi
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E77_Persistent_Itemuri
http://www.researchspace.org/ontology/Thing_instance_of_Classurihttp://www.cidoc-crm.org/cidoc-crm/E22_Man-Made_Objecturi
http://www.researchspace.org/ontology/Thing_refers_to_Thingurihttp://collection.britishmuseum.org/id/object/YCA62958/inscription/1uri
Total: 234, Shown: 20
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SELECT ?p ?o\n", + "WHERE {\n", + " ?p ?o .\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this next query, we look for objects in the collection that have the label 'fibula'." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://collection.britishmuseum.org/sparql
Display: table
" + ], + "text/plain": [ + "Endpoint set to: http://collection.britishmuseum.org/sparql\n", + "Display: table\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
object
http://collection.britishmuseum.org/id/object/GAA14214
http://collection.britishmuseum.org/id/object/BCB9773
http://collection.britishmuseum.org/id/object/WCO94843
http://collection.britishmuseum.org/id/object/WCO94920
http://collection.britishmuseum.org/id/object/WCO94923
http://collection.britishmuseum.org/id/object/WCO95021
http://collection.britishmuseum.org/id/object/WCO95062
http://collection.britishmuseum.org/id/object/WCO95065
http://collection.britishmuseum.org/id/object/WCO96567
http://collection.britishmuseum.org/id/object/WCO152654
Total: 10, Shown: 10
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%endpoint http://collection.britishmuseum.org/sparql\n", + "%display table\n", + "PREFIX bmo: \n", + "PREFIX skos: \n", + "\n", + "SELECT ?object\n", + "WHERE {\n", + "\n", + " # Search for all values of ?object that have a given \"object type\"\n", + " ?object bmo:PX_object_type ?object_type .\n", + "\n", + " # That object type should have the label \"fibula\"\n", + " ?object_type skos:prefLabel \"fibula\" .\n", + "}\n", + "LIMIT 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wikidata\n", + "\n", + "Wikidata is another endpoint we can query. Below we have a query by Sebastian Heath that extracts some of the genealogical data on Roman emperors contained in that database. The `wd:Q842606` can be expanded to refer to [https://www.wikidata.org/wiki/Q842606](https://www.wikidata.org/wiki/Q842606), which describes the concept 'Roman Emperor'. `wdt:P39` is a predicate meaning 'Position held' [https://www.wikidata.org/wiki/Property:P39](https://www.wikidata.org/wiki/Property:P39). " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://query.wikidata.org/sparql
Display: table
" + ], + "text/plain": [ + "Endpoint set to: http://query.wikidata.org/sparql\n", + "Display: table\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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emperorLabelemperor_dobchildLabelmotherLabelmaternalGrandfatherLabelmaternalGrandmotherLabelemperorchildmothermaternalGrandfathermaternalGrandmother
Augustus-062-01-01T00:00:00ZJulia the ElderScriboniaLucius Scribonius Libohttp://www.wikidata.org/entity/Q1405http://www.wikidata.org/entity/Q2259http://www.wikidata.org/entity/Q233444http://www.wikidata.org/entity/Q1159617
Tiberius-041-11-14T00:00:00ZTiberius Julius CaesarJulia the ElderAugustusScriboniahttp://www.wikidata.org/entity/Q1407http://www.wikidata.org/entity/Q4222774http://www.wikidata.org/entity/Q2259http://www.wikidata.org/entity/Q1405http://www.wikidata.org/entity/Q233444
Tiberius-041-11-14T00:00:00ZDrusus Julius CaesarVipsania AgrippinaMarcus Vipsanius AgrippaPomponia Caecilia Atticahttp://www.wikidata.org/entity/Q1407http://www.wikidata.org/entity/Q313737http://www.wikidata.org/entity/Q232090http://www.wikidata.org/entity/Q48174http://www.wikidata.org/entity/Q152626
Claudius-009-07-30T00:00:00ZClaudia OctaviaMessalinaMarcus Valerius Messalla BarbatusDomitia Lepida the Youngerhttp://www.wikidata.org/entity/Q1411http://www.wikidata.org/entity/Q231262http://www.wikidata.org/entity/Q229871http://www.wikidata.org/entity/Q1246807http://www.wikidata.org/entity/Q268964
Claudius-009-07-30T00:00:00ZBritannicusMessalinaMarcus Valerius Messalla BarbatusDomitia Lepida the Youngerhttp://www.wikidata.org/entity/Q1411http://www.wikidata.org/entity/Q313115http://www.wikidata.org/entity/Q229871http://www.wikidata.org/entity/Q1246807http://www.wikidata.org/entity/Q268964
Claudius-009-07-30T00:00:00ZClaudius DrususPlautia UrgulanillaMarcus Plautius Silvanushttp://www.wikidata.org/entity/Q1411http://www.wikidata.org/entity/Q2975271http://www.wikidata.org/entity/Q236259http://www.wikidata.org/entity/Q1232026
Claudius-009-07-30T00:00:00ZClaudia AntoniaAelia PaetinaSextus Aelius Catushttp://www.wikidata.org/entity/Q1411http://www.wikidata.org/entity/Q255410http://www.wikidata.org/entity/Q238448http://www.wikidata.org/entity/Q778051
Vespasian0009-11-15T00:00:00ZTitusDomitilla the ElderFlavius Liberalishttp://www.wikidata.org/entity/Q1419http://www.wikidata.org/entity/Q1421http://www.wikidata.org/entity/Q241102http://www.wikidata.org/entity/Q1114524
Vespasian0009-11-15T00:00:00ZDomitianDomitilla the ElderFlavius Liberalishttp://www.wikidata.org/entity/Q1419http://www.wikidata.org/entity/Q1423http://www.wikidata.org/entity/Q241102http://www.wikidata.org/entity/Q1114524
Vespasian0009-11-15T00:00:00ZDomitilla the YoungerDomitilla the ElderFlavius Liberalishttp://www.wikidata.org/entity/Q1419http://www.wikidata.org/entity/Q260156http://www.wikidata.org/entity/Q241102http://www.wikidata.org/entity/Q1114524
Caligula0012-08-29T00:00:00ZJulia DrusillaMilonia CaesoniaVistiliahttp://www.wikidata.org/entity/Q1409http://www.wikidata.org/entity/Q235586http://www.wikidata.org/entity/Q240928http://www.wikidata.org/entity/Q3658103
Vitellius0015-09-22T00:00:00ZVitellius GermanicusGaleria Fundanahttp://www.wikidata.org/entity/Q1417http://www.wikidata.org/entity/Q662631http://www.wikidata.org/entity/Q260039
Nero0037-12-13T00:00:00ZClaudia AugustaPoppaea SabinaTitus Olliushttp://www.wikidata.org/entity/Q1413http://www.wikidata.org/entity/Q1275952http://www.wikidata.org/entity/Q230716http://www.wikidata.org/entity/Q7810322
Titus0039-12-28T00:00:00ZJulia FlaviaMarcia FurnillaQuintus Marcius Barea Surahttp://www.wikidata.org/entity/Q1421http://www.wikidata.org/entity/Q239314http://www.wikidata.org/entity/Q731059http://www.wikidata.org/entity/Q16203434
Titus0039-12-28T00:00:00ZJulia FlaviaArrecina Tertullahttp://www.wikidata.org/entity/Q1421http://www.wikidata.org/entity/Q239314http://www.wikidata.org/entity/Q1528430
Antoninus Pius0086-09-17T00:00:00ZFaustina the YoungerFaustina the ElderMarcus Annius VerusRupiliahttp://www.wikidata.org/entity/Q1429http://www.wikidata.org/entity/Q236936http://www.wikidata.org/entity/Q234734http://www.wikidata.org/entity/Q745241http://www.wikidata.org/entity/Q2068391
Marcus Aurelius Antoninus0121-04-25T00:00:00ZCommodusFaustina the YoungerAntoninus PiusFaustina the Elderhttp://www.wikidata.org/entity/Q1430http://www.wikidata.org/entity/Q1434http://www.wikidata.org/entity/Q236936http://www.wikidata.org/entity/Q1429http://www.wikidata.org/entity/Q234734
Marcus Aurelius Antoninus0121-04-25T00:00:00ZLucillaFaustina the YoungerAntoninus PiusFaustina the Elderhttp://www.wikidata.org/entity/Q1430http://www.wikidata.org/entity/Q242466http://www.wikidata.org/entity/Q236936http://www.wikidata.org/entity/Q1429http://www.wikidata.org/entity/Q234734
Marcus Aurelius Antoninus0121-04-25T00:00:00ZAnnia Cornificia Faustina MinorFaustina the YoungerAntoninus PiusFaustina the Elderhttp://www.wikidata.org/entity/Q1430http://www.wikidata.org/entity/Q441706http://www.wikidata.org/entity/Q236936http://www.wikidata.org/entity/Q1429http://www.wikidata.org/entity/Q234734
Marcus Aurelius Antoninus0121-04-25T00:00:00ZMarcus Annius Verus CaesarFaustina the YoungerAntoninus PiusFaustina the Elderhttp://www.wikidata.org/entity/Q1430http://www.wikidata.org/entity/Q567222http://www.wikidata.org/entity/Q236936http://www.wikidata.org/entity/Q1429http://www.wikidata.org/entity/Q234734
Total: 82, Shown: 20
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%endpoint http://query.wikidata.org/sparql\n", + "%display table\n", + "\n", + "SELECT ?emperorLabel ?emperor_dob\n", + " ?childLabel\n", + " ?motherLabel ?maternalGrandfatherLabel ?maternalGrandmotherLabel\n", + " ?emperor ?child ?mother ?maternalGrandfather ?maternalGrandmother WHERE {\n", + " \n", + " ?emperor wdt:P39 wd:Q842606 . #p39: position held. Q842606: Roman Emperor\n", + " ?emperor wdt:P569 ?emperor_dob . # p569: date of birth\n", + " ?child wdt:P22 ?emperor . #p22: father\n", + " ?child wdt:P25 ?mother . #p25: mother\n", + " OPTIONAL { ?mother wdt:P22 ?maternalGrandfather }\n", + " OPTIONAL { ?mother wdt:P25 ?maternalGrandmother }\n", + " \n", + " # automatic label expander\n", + " SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n", + " \n", + "} ORDER BY ?emperor_dob\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize these relationships. We're running the same query, but we use CONSTRUCT to create the nodes and edges that represent these familial relationships. We want to show 'emperor x is the father of person y' and 'person a is the mother of person y'. That gives us the structure. To get the content, we run the SELECT command where we first tell it to retrieve those individuals who were emperor, and then retrieve the children data. \n", + "\n", + "Once you've run the query, use ctrl+f to find someone familiar, like Augustus (Q1405). In the resulting graph, an edge labeled 'p22' eg Q1405 ->P22 -> Q2259 can be read, 'Q1405 is the father of Q2259', or rather, 'Augustus is the father of Julia the Elder'.\n", + "\n", + "Roman geneaology.... it's complicated! " + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://query.wikidata.org/sparql
Display: svg
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"\n", + "\n", + "\n", + "\n", + "\n", + "wdt:P22\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node140\n", + "\n", + "Q383304\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node140->node121\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "wdt:P25\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": { + "unconfined": true + }, + "output_type": "display_data" + } + ], + "source": [ + "%endpoint http://query.wikidata.org/sparql\n", + "%display diagram \n", + "\n", + "CONSTRUCT {\n", + " ?emperor wdt:P22 ?child . #p22: father\n", + " ?mother wdt:P25 ?child . #p25: mother \n", + " }\n", + "\n", + "WHERE {\n", + " \n", + " ?emperor wdt:P39 wd:Q842606 .\n", + " ?child wdt:P22 ?emperor . #p22: father\n", + " ?child wdt:P25 ?mother . #p25: mother\n", + " OPTIONAL { ?mother wdt:P22 ?maternalGrandfather }\n", + " OPTIONAL { ?mother wdt:P25 ?maternalGrandmother }\n", + "} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nomisma\n", + "\n", + "Another excellent SPARQL endpoint is the Nomisma portal for numismatic materials. \n", + "\n", + "http://nomisma.org/sparql" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://nomisma.org/query
" + ], + "text/plain": [ + "Endpoint set to: http://nomisma.org/query\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%endpoint http://nomisma.org/query" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, if you actually go to [http://query.wikidata.org/sparql](http://query.wikidata.org/sparql) you'll find a query builder with the following information already preloaded:\n", + "\n", + "```\n", + "PREFIX rdf:\t\n", + "PREFIX bio:\t\n", + "PREFIX crm:\t\n", + "PREFIX dcmitype:\t\n", + "PREFIX dcterms:\t\n", + "PREFIX foaf:\t\n", + "PREFIX geo:\t\n", + "PREFIX nm:\t\n", + "PREFIX nmo:\t\n", + "PREFIX org:\t\n", + "PREFIX osgeo:\t\n", + "PREFIX rdac:\t\n", + "PREFIX skos:\t\n", + "PREFIX spatial: \n", + "PREFIX void:\t\n", + "PREFIX xsd:\t\n", + "\n", + "SELECT * WHERE {\n", + " ?s ?p ?o\n", + "} LIMIT 100\n", + "```\n", + "\n", + "All those prefixes are the ontologies being used to describe the materials. The `?s ?p ?o` are the subject, predicate, objects that we're going to search for. Let's run some of the [example queries](http://nomisma.org/documentation/sparql) that Nomisma can handle. Since Roman Emperors are often depicted on coins, let's see which emperors are present in Nomisma." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Display: table
" + ], + "text/plain": [ + "Display: table\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
urilabel
http://nomisma.org/id/vabalathusVabalathus
http://nomisma.org/id/valerius_valensValerius Valens
http://nomisma.org/id/arcadiusArcadius
http://nomisma.org/id/carausiusCarausius
http://nomisma.org/id/valerianValerian
http://nomisma.org/id/commodusCommodus
http://nomisma.org/id/justinian_iJustinian I
http://nomisma.org/id/tetricus_iiTetricus II
http://nomisma.org/id/nervaNerva
http://nomisma.org/id/olybriusOlybrius
http://nomisma.org/id/volusianVolusian
http://nomisma.org/id/sextus_martinianusMartinianus
http://nomisma.org/id/leo_iiLeo II
http://nomisma.org/id/elagabalusElagabalus
http://nomisma.org/id/tiberiusTiberius
http://nomisma.org/id/sebastianusSebastianus
http://nomisma.org/id/julius_neposJulius Nepos
http://nomisma.org/id/numerianNumerian
http://nomisma.org/id/constantius_iiConstantius II
http://nomisma.org/id/maximus_barcelonaMaximus of Barcelona
Total: 128, Shown: 20
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%display table \n", + "PREFIX rdf:\t\n", + "PREFIX bio:\t\n", + "PREFIX crm:\t\n", + "PREFIX dcmitype:\t\n", + "PREFIX dcterms:\t\n", + "PREFIX foaf:\t\n", + "PREFIX geo:\t\n", + "PREFIX nm:\t\n", + "PREFIX nmo:\t\n", + "PREFIX org:\t\n", + "PREFIX osgeo:\t\n", + "PREFIX rdac:\t\n", + "PREFIX skos:\t\n", + "PREFIX spatial: \n", + "PREFIX void:\t\n", + "PREFIX xsd:\t\n", + "\n", + "SELECT ?uri ?label WHERE {\n", + "?uri a foaf:Person ;\n", + " skos:prefLabel ?label ; \n", + " org:hasMembership ?membership .\n", + "?membership org:role nm:roman_emperor .\n", + "FILTER(langMatches(lang(?label), \"EN\"))\n", + "} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also do spatial queries; this one looks coins from mints within 50 km of Athens.\n", + "\n", + "It also specifies the format in which we wants the results returned, and to write these results to a json file for further manipulation." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: JSON
Display: table
Output file: mints.json
" + ], + "text/plain": [ + "Return format: JSON\n", + "Display: table\n", + "Output file: mints.json\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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loclatlongmintlabel
http://nomisma.org/id/rome#this41.912.5http://nomisma.org/id/romeRome
http://nomisma.org/id/caesareia_cappadocia#this38.73333335.483333http://nomisma.org/id/caesareia_cappadociaCaesarea in Cappadocia
http://nomisma.org/id/nicomedia#this40.76666729.916667http://nomisma.org/id/nicomediaNicomedia
http://nomisma.org/id/siscia#this45.48316816.371388http://nomisma.org/id/sisciaSiscia
http://nomisma.org/id/emisa#this34.75189936.724237http://nomisma.org/id/emisaEmisa
http://nomisma.org/id/viminacium#this44.71647121.166605http://nomisma.org/id/viminaciumViminacium
http://nomisma.org/id/panticapaeum#this45.33861136.468056http://nomisma.org/id/panticapaeumPanticapaeum
http://nomisma.org/id/phanagoria#this45.18936.825http://nomisma.org/id/phanagoriaPhangoria
http://nomisma.org/id/thasos#this40.77762624.703702http://nomisma.org/id/thasosThasos
http://nomisma.org/id/thessalian_league#this39.64166722.416667http://nomisma.org/id/thessalian_leagueThessalian League
http://nomisma.org/id/agrigentum#this37.31666713.583333http://nomisma.org/id/agrigentumAgrigentum
http://nomisma.org/id/gela#this37.06315614.258219http://nomisma.org/id/gelaGela
http://nomisma.org/id/leontini#this37.28547814.998115http://nomisma.org/id/leontiniLeontini
http://nomisma.org/id/messana#this38.19225115.556634http://nomisma.org/id/messanaMessana
http://nomisma.org/id/syracuse#this37.08333315.283333http://nomisma.org/id/syracuseSyracuse
http://nomisma.org/id/parthia#this32.324275653.1738281http://nomisma.org/id/parthiaParthia
http://nomisma.org/id/seleuceia_ad_tigrim#this33.13722244.517222http://nomisma.org/id/seleuceia_ad_tigrimSeleuceia ad Tigrim
http://nomisma.org/id/antiocheia_syria#this36.236.15http://nomisma.org/id/antiocheia_syriaAntioch
http://nomisma.org/id/tarsus#this36.91666734.9http://nomisma.org/id/tarsusTarsus
http://nomisma.org/id/massalia#this43.2968545.382499http://nomisma.org/id/massaliaMassalia
Total: 1798, Shown: 20
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%format json \n", + "%display table \n", + "%outfile mints.json \n", + "PREFIX rdf:\t\n", + "PREFIX dcterms:\t\n", + "PREFIX geo:\t\n", + "PREFIX nm:\t\n", + "PREFIX nmo:\t\n", + "PREFIX skos:\t\n", + "PREFIX spatial: \n", + "PREFIX xsd:\t\n", + "\n", + "SELECT * WHERE {\n", + " ?loc spatial:nearby (37.974722 23.7225 50 'km') ;\n", + " geo:lat ?lat ;\n", + " geo:long ?long .\n", + " ?mint geo:location ?loc ;\n", + " skos:prefLabel ?label ;\n", + " a nmo:Mint\n", + " FILTER langMatches (lang(?label), 'en')\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SPARQL", + "language": "sparql", + "name": "sparql" + }, + "language_info": { + "codemirror_mode": { + "name": "sparql" + }, + "mimetype": "application/sparql-query", + "name": "sparql", + "pygments_lexer": "sparql-nb" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lod/tutorial/sparql-vanGogh.ipynb b/lod/tutorial/sparql-vanGogh.ipynb new file mode 100644 index 0000000..642a866 --- /dev/null +++ b/lod/tutorial/sparql-vanGogh.ipynb @@ -0,0 +1,2513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SPARQL about van Gogh\n", + "\n", + "
Note: when viewed through nbviewer or converted through nbconvert this notebook looks slightly different than when rendered by a running sparqlkernel. See a converted version for an example more similar to the actual look in a \"live\" kernel.
\n", + "\n", + "We are going to use some the [DBPedia SPARQL endpoint](http://wiki.dbpedia.org/OnlineAccess#1.1%20Public%20SPARQL%20Endpoint) to do some queries related to _Vincent van Gogh_.\n", + "\n", + "First we define the endpoint and the preferred language for labels" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Endpoint set to: http://dbpedia.org/sparql
Label preferred languages: ['en']
Logging set to DEBUG
" + ], + "text/plain": [ + "Endpoint set to: http://dbpedia.org/sparql\n", + "Label preferred languages: ['en']\n", + "Logging set to DEBUG\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%endpoint http://dbpedia.org/sparql\n", + "%lang en\n", + "\n", + "# This is optional, it would increase the log level.\n", + "# The default logfile (unless changed upon kernel installation) is [TMPDIR]/sparqlkernel.log, \n", + "# where [TMPDIR] is the platform default temporal directory\n", + "%log debug" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's find out the entity URI for _van Gogh_ in DBPedia. We search entities that are persons and whose label contains `van Gogh` (case insensitive).\n", + "\n", + "Note that the DBPedia endpoint has a set of [predefined namespace prefixes](http://dbpedia.org/sparql?nsdecl) that we can use without the need to define them in the query, such as `rdfs:` or `foaf:` so we could remove them from the query without problem." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 4)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m PREFIX rdfs: \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "%format json\n", + "%display table \n", + "\n", + "PREFIX rdfs: \n", + "PREFIX foaf: \n", + "SELECT ?person ?name\n", + "WHERE {\n", + " ?person a foaf:Person .\n", + " ?person rdfs:label ?name\n", + " FILTER regex(?name,\"van gogh\",\"i\")\n", + " FILTER langMatches(lang(?name),\"en\")\n", + "} LIMIT 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear from the result that we want the DBPedia entity (we can click on the link for confirmation, and it will lead us to a DBPedia web page describing the resource).\n", + "\n", + "## Fact finding\n", + "\n", + "Now that we found his URI, let's search for places, dates and people related with van Gogh.\n", + "\n", + "In this query we also set the table to show the data type for each result" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: JSON
Display: table
Show Types: on
" + ], + "text/plain": [ + "Return format: JSON\n", + "Display: table\n", + "Show Types: on\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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proptypevaluetype
http://dbpedia.org/ontology/birthDateuri1853-03-30typed-literal
http://dbpedia.org/ontology/birthPlaceurihttp://dbpedia.org/resource/Zunderturi
http://dbpedia.org/ontology/birthPlaceurihttp://dbpedia.org/resource/Netherlandsuri
http://dbpedia.org/ontology/deathDateuri1890-07-29typed-literal
http://dbpedia.org/ontology/deathPlaceurihttp://dbpedia.org/resource/French_Third_Republicuri
http://dbpedia.org/ontology/deathPlaceurihttp://dbpedia.org/resource/Auvers-sur-Oiseuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Israel_Tsvaygenbaumuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Victor_Stabinuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Leo_Marchutzuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Annette_P._Jimersonuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Aung_Khinuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Joan_Glassuri
http://dbpedia.org/ontology/influencedByurihttp://dbpedia.org/resource/Chuck_Connellyuri
http://dbpedia.org/ontology/relativeurihttp://dbpedia.org/resource/Theo_van_Gogh_(film_director)uri
http://dbpedia.org/property/influencedurihttp://dbpedia.org/resource/Adolphe_Monticelliuri
http://dbpedia.org/property/influencedByurihttp://dbpedia.org/resource/Axel_Törnemanuri
http://dbpedia.org/property/influencedByurihttp://dbpedia.org/resource/Hayley_Leveruri
http://dbpedia.org/property/influencedByurihttp://dbpedia.org/resource/Armanuri
http://dbpedia.org/property/influencedByurihttp://dbpedia.org/resource/Roger_Inguri
http://dbpedia.org/property/influencedByurihttp://dbpedia.org/resource/Purvis_Younguri
Total: 35, Shown: 20
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%format json\n", + "%display table withtypes\n", + "\n", + "# We might have more than one triple pointing to the same object, so we group by object\n", + "# and take one arbitrary predicate\n", + "\n", + "SELECT (SAMPLE(?pred) AS ?prop) ?value\n", + "WHERE {\n", + "\n", + " # Places and dates\n", + " {\n", + " dbr:Vincent_van_Gogh ?pred ?value .\n", + " { ?pred rdfs:range xsd:date }\n", + " UNION\n", + " { ?pred rdfs:range dbo:Place }\n", + " }\n", + " # People van Gogh relates to\n", + " UNION \n", + " { \n", + " dbr:Vincent_van_Gogh ?pred ?value .\n", + " ?value a foaf:Person \n", + " }\n", + " # People related to van Gogh\n", + " UNION \n", + " { \n", + " ?value ?pred dbr:Vincent_van_Gogh .\n", + " ?value a foaf:Person \n", + " }\n", + "\n", + "} GROUP BY ?value ORDER BY ?prop\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Find a painting\n", + "Now we search for a van Gogh painting whose title contains _\"night\"_" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: JSON
Display: table
" + ], + "text/plain": [ + "Return format: JSON\n", + "Display: table\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
picturename
http://dbpedia.org/resource/Starry_Night_Over_the_RhoneStarry Night Over the Rhone
http://dbpedia.org/resource/Cafe_Terrace_at_NightCafe Terrace at Night
http://dbpedia.org/resource/The_Night_CaféThe Night Café
http://dbpedia.org/resource/The_Starry_NightThe Starry Night
http://dbpedia.org/resource/White_House_at_NightWhite House at Night
Total: 5, Shown: 5
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%format json\n", + "%display table \n", + "\n", + "\n", + "PREFIX rdfs: \n", + "PREFIX foaf: \n", + "SELECT DISTINCT (?picture) ?name\n", + "WHERE {\n", + " ?picture ?p1 dbr:Vincent_van_Gogh .\n", + " ?picture rdfs:label ?name .\n", + " FILTER regex(?name,\"night\", \"i\")\n", + " FILTER langMatches(lang(?name),\"en\") \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to show another possibility, we repeat the same query but indicating `raw` display format" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Display: raw
" + ], + "text/plain": [ + "Display: raw\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'head': {'link': [], 'vars': ['picture', 'name']},\n", + " 'results': {'bindings': [{'name': {'type': 'literal',\n", + " 'value': 'Starry Night Over the Rhone',\n", + " 'xml:lang': 'en'},\n", + " 'picture': {'type': 'uri',\n", + " 'value': 'http://dbpedia.org/resource/Starry_Night_Over_the_Rhone'}},\n", + " {'name': {'type': 'literal',\n", + " 'value': 'Cafe Terrace at Night',\n", + " 'xml:lang': 'en'},\n", + " 'picture': {'type': 'uri',\n", + " 'value': 'http://dbpedia.org/resource/Cafe_Terrace_at_Night'}},\n", + " {'name': {'type': 'literal',\n", + " 'value': 'The Night Café',\n", + " 'xml:lang': 'en'},\n", + " 'picture': {'type': 'uri',\n", + " 'value': 'http://dbpedia.org/resource/The_Night_Café'}},\n", + " {'name': {'type': 'literal',\n", + " 'value': 'The Starry Night',\n", + " 'xml:lang': 'en'},\n", + " 'picture': {'type': 'uri',\n", + " 'value': 'http://dbpedia.org/resource/The_Starry_Night'}},\n", + " {'name': {'type': 'literal',\n", + " 'value': 'White House at Night',\n", + " 'xml:lang': 'en'},\n", + " 'picture': {'type': 'uri',\n", + " 'value': 'http://dbpedia.org/resource/White_House_at_Night'}}],\n", + " 'distinct': False,\n", + " 'ordered': True}}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%display raw \n", + "\n", + "PREFIX rdfs: \n", + "PREFIX foaf: \n", + "SELECT DISTINCT (?picture) ?name\n", + "WHERE {\n", + " ?picture ?p1 dbr:Vincent_van_Gogh .\n", + " ?picture rdfs:label ?name .\n", + " FILTER regex(?name,\"night\", \"i\")\n", + " FILTER langMatches(lang(?name),\"en\") \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Describe a painting\n", + "\n", + "Now we find out all that DBPedia knows about one of those paintings, _Starry Night Over the Rhone_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: N3
Display: table
Label preferred languages: ['en']
Result maximum size: unlimited
" + ], + "text/plain": [ + "Return format: N3\n", + "Display: table\n", + "Label preferred languages: ['en']\n", + "Result maximum size: unlimited\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
subjectpredicateobject
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Art102743547
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2003/01/geo/wgs84_pos#SpatialThing
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/ns/prov#wasDerivedFromhttp://en.wikipedia.org/wiki/Starry_Night_Over_the_Rhone?oldid=676277356
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/typehttp://dbpedia.org/resource/Oil_painting
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://xmlns.com/foaf/0.1/isPrimaryTopicOfhttp://en.wikipedia.org/wiki/Starry_Night_Over_the_Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/artisthttp://dbpedia.org/resource/Vincent_van_Gogh
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://purl.org/voc/vrank#hasRankfe92858e2aced48778456b33b2d46291ab1
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/titleStarry Night over the Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/cityhttp://dbpedia.org/resource/Paris
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/ontology/thumbnailhttp://commons.wikimedia.org/wiki/Special:FilePath/Starry_Night_Over_the_Rhone.jpg?width=300
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/ontology/Artwork
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/2000/01/rdf-schema#labelStarry Night Over the Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/PhysicalEntity100001930
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2002/07/owl#Thing
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/ontology/Work
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/catalogueF474; JH1592
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://purl.org/dc/terms/subjecthttp://dbpedia.org/resource/Category:Collections_of_the_Musée_d'Orsay
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/1888Paintings
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Artifact100021939
http://en.wikipedia.org/wiki/Starry_Night_Over_the_Rhonehttp://xmlns.com/foaf/0.1/primaryTopichttp://dbpedia.org/resource/Starry_Night_Over_the_Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/VincentVanGoghPaintingsOfArles
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/imperialUnitin
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/ontology/authorhttp://dbpedia.org/resource/Vincent_van_Gogh
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://xmlns.com/foaf/0.1/nameStarry Night over the Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/ontology/museumhttp://dbpedia.org/resource/Musée_d'Orsay
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/imageFileStarry Night Over the Rhone.jpg
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/metricUnitcm
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://schema.org/CreativeWork
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Painting103876519
http://dbpedia.org/resource/Starry_Night_Over_the_Rhônehttp://dbpedia.org/ontology/wikiPageRedirectshttp://dbpedia.org/resource/Starry_Night_Over_the_Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/2000/01/rdf-schema#commentStarry Night Over the Rhone (September 1888) is one of Vincent van Gogh's paintings of Arles at night time. It was painted at a spot on the bank of the Rhone River that was only a one or two-minute walk from the Yellow House on the Place Lamartine which Van Gogh was renting at the time.
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://purl.org/dc/terms/subjecthttp://dbpedia.org/resource/Category:1888_paintings
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.wikidata.org/entity/Q386724
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Object100002684
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/GraphicArt103453809
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Creation103129123
http://dbpedia.org/resource/Starry_Night_over_the_Rhonehttp://dbpedia.org/ontology/wikiPageRedirectshttp://dbpedia.org/resource/Starry_Night_Over_the_Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://xmlns.com/foaf/0.1/depictionhttp://commons.wikimedia.org/wiki/Special:FilePath/Starry_Night_Over_the_Rhone.jpg
http://dbpedia.org/resource/Starry_Night_(disambiguation)http://dbpedia.org/ontology/wikiPageDisambiguateshttp://dbpedia.org/resource/Starry_Night_Over_the_Rhone
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/PaintingsByVincentVanGogh
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/class/yago/Whole100003553
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://purl.org/dc/terms/subjecthttp://dbpedia.org/resource/Category:Paintings_by_Vincent_van_Gogh
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/ontology/abstractStarry Night Over the Rhone (September 1888) is one of Vincent van Gogh's paintings of Arles at night time. It was painted at a spot on the bank of the Rhone River that was only a one or two-minute walk from the Yellow House on the Place Lamartine which Van Gogh was renting at the time. The night sky and the effects of light at night provided the subject for some of his more famous paintings, including Cafe Terrace at Night (painted earlier the same month) and the later canvas from Saint-Rémy, The Starry Night.A sketch of the painting is included in a letter van Gogh sent to his friend Eugène Boch on October 2, 1888.The painting was first exhibited in 1889 at the annual exhibition of the Société des Artistes Indépendants in Paris, together with the Irises. The latter was added by Theo, while Vincent had proposed one of his paintings from the public gardens in Arles, most probably the version now in the Phillips Collection.
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://dbpedia.org/property/museumhttp://dbpedia.org/resource/Musée_d'Orsay
http://dbpedia.org/resource/Starry_Night_Over_the_Rhonehttp://purl.org/dc/terms/subjecthttp://dbpedia.org/resource/Category:Vincent_van_Gogh_paintings_of_Arles
Shown: all, Total rows: 80
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%format n3\n", + "%display table\n", + "%lang en\n", + "%show all\n", + "\n", + "PREFIX rdfs: \n", + "PREFIX foaf: \n", + "CONSTRUCT { ?s1 ?p1 dbr:Starry_Night_Over_the_Rhone .\n", + " dbr:Starry_Night_Over_the_Rhone ?p2 ?o1 .\n", + " }\n", + "WHERE {\n", + " { ?s1 ?p1 dbr:Starry_Night_Over_the_Rhone }\n", + " UNION\n", + " { dbr:Starry_Night_Over_the_Rhone ?p2 ?o1 }\n", + " \n", + " FILTER ( ?p1 != owl:sameAs && ?p2 != owl:sameAs)\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar data could be obtained by asking the endpoint to `DESCRIBE` the resource. Let's do this, but for a twist let's ask the kernel to draw a graph with the results (for this to work, [Graphviz](http://www.graphviz.org/) should be installed in the system).\n", + "\n", + "We generate a PNG here. Note that `svg` is a much better format than `png`, if the browser supports it (better quality, and hyperlinked nodes)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: N3
Display: png
Label preferred languages: ['en']
" + ], + "text/plain": [ + "Return format: N3\n", + "Display: png\n", + "Label preferred languages: ['en']\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ }, + "metadata": { + "image/png": { + "height": 2200, + "unconfined": true, + "width": 5500 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%format n3\n", + "%display diagram png\n", + "%lang en\n", + "\n", + "PREFIX rdfs: \n", + "PREFIX foaf: \n", + "DESCRIBE dbr:Starry_Night_Over_the_Rhone" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Locate paintings\n", + "\n", + "Finally, to print out a slightly more complex graph, we will construct one with all of van Gogh's paintings, together with their location and the country they are in.\n", + "\n", + "This one we render as SVG; this has the said advantage of better quality (being a vector format). Plus nodes and edges can contain hyperlinks, so when they are URIs they point to the full URL (note that, unless the `withliterals` option is used, **all** represented nodes will be URIs)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Return format: N3
Display: svg
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?subject .\n", + " ?museum dbo:location ?location .\n", + " ?location dbo:country ?country .\n", + " }\n", + "WHERE {\n", + " ?painting dbp:artist dbr:Vincent_van_Gogh .\n", + " ?painting a wd:Q386724 .\n", + " ?painting a dbo:Artwork .\n", + " ?painting dbp:museum ?museum .\n", + " ?museum dbo:location ?location .\n", + " ?location dbo:country ?country .\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Version: 1.1 (2016-08-10)
\n", + "Author: Paulo Villegas
" + ] + } + ], + "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, + "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": "3.7.9" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}