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Created March 13, 2018 08:23
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An intro to using RDFLib and triples in Python
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to semantics: Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code below assumes a semantic store endpoint is available at `localhost:3030` which corresponds to the default of a local [Fuseki server](https://jena.apache.org/index.html). The code is actually independent of Fuseki and based on SPARQL 1.1; you can run an [AllegroGraph service in a docker container](https://franz.com/agraph/docker/) or a remote [Stardog service](https://www.stardog.com).\n",
"\n",
"The [rdflib](http://rdflib.readthedocs.io/en/stable/) is just a pip away if not installed\n",
"\n",
" pip install rdflib\n",
"\n",
"as well as the sparqlwrapper:\n",
"\n",
" pip install sparqlwrapper\n",
" \n",
"to connect to the service. \n",
"\n",
"The query and update endpoints are separate but you can unify them in one object like so:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/swa/conda/lib/python3.6/site-packages/SPARQLWrapper/Wrapper.py:438: UserWarning: keepalive support not available, so the execution of this method has no effect\n",
" warnings.warn(\"keepalive support not available, so the execution of this method has no effect\")\n"
]
}
],
"source": [
"from SPARQLWrapper import SPARQLWrapper, JSON\n",
"from rdflib.plugins.stores.sparqlstore import SPARQLUpdateStore\n",
"import rdflib\n",
"ENDPOINT = \"http://localhost:3030/Test\"\n",
"\n",
"\n",
"store = SPARQLUpdateStore(\n",
" queryEndpoint=f\"{ENDPOINT}/sparql\",\n",
" update_endpoint=f\"{ENDPOINT}/update\",\n",
" context_aware=False)\n",
"store.setReturnFormat(JSON)"
]
},
{
"cell_type": "markdown",
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"source": [
"This allows you to insert data straight away:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"store.update('Insert data {<http://www.orbifold.net/swa> <http://www.orbifold.net/is> <http://www.orbifold.net/scientist>}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and to query it one would use something like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"\n",
"q = \"\"\"\n",
" PREFIX home: <http://www.orbifold.net/>\n",
" SELECT ?p\n",
" WHERE { home:swa ?p ?o }\n",
"\"\"\" \n",
"results = store.query(q)\n",
"\n",
"bindings = results.bindings\n",
"if bindings is None or len(bindings) == 0: \n",
" print(\"No results\")\n",
"results = list()\n",
"for r in bindings: \n",
" print(r[rdflib.Variable('p')].toPython())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the result set is a dictionary with a `rdflib.Vairable` key, not a string. The query can also be copy/pates in [YASGUI](http://yasgui.org) provided you alter the dropdown box with the appropriate endpoint."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"q = \"\"\" \n",
" SELECT (count(*) as ?c)\n",
" WHERE { ?s ?p ?o }\n",
"\"\"\" \n",
"results = store.query(q)\n",
"\n",
"bindings = results.bindings\n",
"if bindings is None or len(bindings) == 0: \n",
" print(\"No results\")\n",
"print(bindings[0][rdflib.Variable('c')].toPython())\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"source": [
"The `toPython` method is a utility converting uniformly URI's and literals for you. \n",
"\n",
"Let's add some data to our store first so we can demo some more techniques with it. The [iris dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data) is the hello-world equivalent in data science and is useful for some ideas described later on: "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
"names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
"dataset = pandas.read_csv(url, names=names)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal-length</th>\n",
" <th>sepal-width</th>\n",
" <th>petal-length</th>\n",
" <th>petal-width</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal-length sepal-width petal-length petal-width class\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How to push this typical tabular data in a semantic store? There are various ways, below is the fairly standard star-graph type of storage."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"for i in dataset.index:\n",
" q = f\"\"\"\n",
" PREFIX iris: <http://www.orbifold.net/iris/>\n",
" PREFIX item: <http://www.orbifold.net/iris/item/>\n",
" INSERT DATA {{\n",
" iris:data iris:contains item:{i}.\n",
" item:{i} iris:sepal_length \"{dataset.iloc[i][\"sepal-length\"]}\".\n",
" item:{i} iris:sepal_width \"{dataset.iloc[i][\"sepal-width\"]}\".\n",
" item:{i} iris:petal_length \"{dataset.iloc[i][\"petal-length\"]}\".\n",
" item:{i} iris:petal_width \"{dataset.iloc[i][\"petal-width\"]}\".\n",
" item:{i} iris:class \"{dataset.iloc[i][\"class\"]}\".\n",
" }}\n",
" \"\"\" \n",
" store.update(q)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To extract these records back via SPARQL you need to use a subquery, somethind like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"q = \"\"\"\n",
" PREFIX iris: <http://www.orbifold.net/iris/>\n",
" PREFIX item: <http://www.orbifold.net/iris/item/>\n",
" SELECT ?s ?k ?m \n",
" where {\n",
" ?s ?k ?m\n",
" {\n",
" select ?s \n",
" {\n",
" <http://www.orbifold.net/iris/data> <http://www.orbifold.net/iris/contains> ?s\n",
" }\n",
" }\n",
" }\n",
" LIMIT 2\n",
"\"\"\"\n",
"results = store.query(q)\n",
"\n",
"bindings = results.bindings\n",
"if bindings is None or len(bindings) == 0: \n",
" print(\"No results\")\n",
"for r in bindings: \n",
" print(r.values())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about graphing these results? This can be easily done with NetworkX but requires first a bit of stripping. The URI's are too verbose for a graph, so let's reduce things a bit:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class Iris:\n",
" def __init__(self):\n",
" self.id = -1\n",
" self.petal_length = -1\n",
" self.petal_width = -1\n",
" self.sepal_length = -1\n",
" self.sepal_width = -1\n",
" self.type = None\n",
" \n",
"q = \"\"\"\n",
" PREFIX iris: <http://www.orbifold.net/iris/>\n",
" PREFIX item: <http://www.orbifold.net/iris/item/>\n",
" SELECT ?s ?k ?m \n",
" where {\n",
" ?s ?k ?m\n",
" {\n",
" select ?s \n",
" {\n",
" <http://www.orbifold.net/iris/data> <http://www.orbifold.net/iris/contains> ?s\n",
" }\n",
" }\n",
" }\n",
" LIMIT 1000\n",
"\"\"\"\n",
"results = store.query(q)\n",
"\n",
"bindings = results.bindings\n",
"flowers = {}\n",
"if bindings is None or len(bindings) == 0: \n",
" print(\"No results\")\n",
"for r in bindings: \n",
" id = int(r[rdflib.Variable('s')].toPython().replace(\"http://www.orbifold.net/iris/item/\", \"\"))\n",
" if id in flowers.keys():\n",
" flower = flowers[id]\n",
" else:\n",
" flower = Iris() \n",
" flowers[id]= flower\n",
" prop_name = r[rdflib.Variable('k')].toPython().replace(\"http://www.orbifold.net/iris/\", \"\")\n",
" # the propname 'class' is a reserved word\n",
" if prop_name == \"class\": prop_name = \"type\"\n",
" prop_value = r[rdflib.Variable('m')].toPython()\n",
" vars(flower)[prop_name] = prop_value\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that technique shows you:\n",
"\n",
"- how to convert triples to standard record sets\n",
"- how to strip noise away\n",
"- how semantic data can be converted to tabular data ready for machine learning\n",
"\n",
"Now, with some NetworkX API you can easily draw the flower network (or part of it):"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x151f345278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import networkx as nx\n",
"%matplotlib inline\n",
"G = nx.Graph()\n",
"rec = []\n",
"for i in range(30):\n",
" v = vars(flowers[i])\n",
" for key in v.keys():\n",
" rec.append((f\"f{i}\", v[key]))\n",
" \n",
" \n",
" G.add_edges_from(rec)\n",
"# labels = {}\n",
"cols = [\"red\" if str(n)[0]==\"f\" else \"green\" for n in G.nodes() ]\n",
"pos = nx.layout.kamada_kawai_layout(G)\n",
"nx.draw(G, pos=pos, with_labels= True, node_color= cols, edge_color=\"silver\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are of course many ways to approach the data, with [H2O Flow](https://www.h2o.ai/h2o-old/h2o-flow/) with [Zeppelin](https://zeppelin.apache.org) and so on."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another way to visualize things is with YASGUI. For example, after having pushed the iris data above you can visualize the distribution of a feature:\n",
"\n",
"![YASGUI dataviz](http://www.orbifold.net/default/wp-content/uploads/2018/03/YasguiDataviz.png)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many records do we have in the triple store?"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"150\n"
]
}
],
"source": [
"\n",
"q = f\"\"\"\n",
"select (COUNT(*) AS ?count) where {{\n",
" ?s <http://www.orbifold.net/iris/contains> ?o. \n",
"}}\t\n",
"\"\"\"\n",
"results = store.query(q)\n",
"print (int(results.bindings[0][rdflib.Variable('count')].value))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many have the word 'setosa'?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50\n"
]
}
],
"source": [
"\n",
"q = f\"\"\"\n",
"select (COUNT(*) AS ?count) where {{\n",
" ?s <http://www.orbifold.net/iris/class> ?o. \n",
" FILTER (contains( str(?o), \"setosa\"))\n",
"}}\n",
"\"\"\"\n",
"results = store.query(q)\n",
"print (int(results.bindings[0][rdflib.Variable('count')].value))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are plenty of other fun things you can do with triples and Python but I'd hope this gets you started."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"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": "3.6.3"
},
"nteract": {
"version": "0.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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