Analysis-Concepts-Size-Inference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_inf = pd.read_csv(\"results/concept-size-distribution/concept-distribution.tsv\", sep='\\t',low_memory=True, engine='python')\n",
    "df_with_inf = pd.read_csv(\"results/concept-size-distribution/concept-distribution-inference.csv\",sep=';~;',low_memory=True,engine='python')\n",
    "df_mapped_concepts = pd.read_csv(\"results/concept-size-distribution/mapped-concepts-distribution.csv\", sep=\";\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>concept</th>\n",
       "      <th>nb_instances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>497994</td>\n",
       "      <td>http://purl.org/linked-data/cube#Observation</td>\n",
       "      <td>1306389396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163701</td>\n",
       "      <td>http://data-gov.tw.rpi.edu/2009/data-gov-twc.r...</td>\n",
       "      <td>304878654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>313862</td>\n",
       "      <td>http://geovocab.org/geometry#Geometry</td>\n",
       "      <td>167808111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id                                            concept  nb_instances\n",
       "0  497994       http://purl.org/linked-data/cube#Observation    1306389396\n",
       "1  163701  http://data-gov.tw.rpi.edu/2009/data-gov-twc.r...     304878654\n",
       "2  313862              http://geovocab.org/geometry#Geometry     167808111"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_inf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>concept_uri</th>\n",
       "      <th>nb_instances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://bioonto.de/mesh.owl#C433704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.geovista.psu.edu/cv/geon_themes#Bor...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://lodlaundromat.org/.well-known/genid/fab...</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   row_id                                        concept_uri  nb_instances\n",
       "0       0                 http://bioonto.de/mesh.owl#C433704             0\n",
       "1       1  http://www.geovista.psu.edu/cv/geon_themes#Bor...             0\n",
       "2       2  http://lodlaundromat.org/.well-known/genid/fab...             0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_inf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>concept_id</th>\n",
       "      <th>concept_uri</th>\n",
       "      <th>nb_instances</th>\n",
       "      <th>inference</th>\n",
       "      <th>comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1325</td>\n",
       "      <td>http://www.w3.org/2000/01/rdf-schema#Class</td>\n",
       "      <td>469396087</td>\n",
       "      <td>(with</td>\n",
       "      <td>inf)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1326</td>\n",
       "      <td>http://www.w3.org/2002/07/owl#Class</td>\n",
       "      <td>469396087</td>\n",
       "      <td>(with</td>\n",
       "      <td>inf)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1337</td>\n",
       "      <td>http://www.w3.org/2002/07/owl#Thing</td>\n",
       "      <td>469396087</td>\n",
       "      <td>(with</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   concept_id                                 concept_uri  nb_instances  \\\n",
       "0        1325  http://www.w3.org/2000/01/rdf-schema#Class     469396087   \n",
       "1        1326         http://www.w3.org/2002/07/owl#Class     469396087   \n",
       "2        1337         http://www.w3.org/2002/07/owl#Thing     469396087   \n",
       "\n",
       "  inference comment  \n",
       "0     (with    inf)  \n",
       "1     (with    inf)  \n",
       "2     (with    inf)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapped_concepts.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>concept</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>497994</td>\n",
       "      <td>http://purl.org/linked-data/cube#Observation</td>\n",
       "      <td>1306389396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163701</td>\n",
       "      <td>http://data-gov.tw.rpi.edu/2009/data-gov-twc.r...</td>\n",
       "      <td>304878654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>313862</td>\n",
       "      <td>http://geovocab.org/geometry#Geometry</td>\n",
       "      <td>167808111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122406</td>\n",
       "      <td>http://knoesis.wright.edu/ssw/ont/sensor-obser...</td>\n",
       "      <td>144044989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>513368</td>\n",
       "      <td>http://xmlns.com/foaf/0.1/Person</td>\n",
       "      <td>132919327</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   row_id                                            concept  nb_instances\n",
       "0  497994       http://purl.org/linked-data/cube#Observation    1306389396\n",
       "1  163701  http://data-gov.tw.rpi.edu/2009/data-gov-twc.r...     304878654\n",
       "2  313862              http://geovocab.org/geometry#Geometry     167808111\n",
       "3  122406  http://knoesis.wright.edu/ssw/ont/sensor-obser...     144044989\n",
       "4  513368                   http://xmlns.com/foaf/0.1/Person     132919327"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_inf_sorted =  df_no_inf.sort_values(by='nb_instances', ascending=False)\n",
    "df_no_inf_sorted.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>concept_uri</th>\n",
       "      <th>nb_instances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2357292</th>\n",
       "      <td>2357292</td>\n",
       "      <td>http://purl.org/linked-data/cube#Attachable</td>\n",
       "      <td>1306578332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46156</th>\n",
       "      <td>46156</td>\n",
       "      <td>http://purl.org/linked-data/cube#Observation</td>\n",
       "      <td>1306389397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946860</th>\n",
       "      <td>1946860</td>\n",
       "      <td>http://www.w3.org/1999/02/22-rdf-syntax-ns#Res...</td>\n",
       "      <td>469782792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2728207</th>\n",
       "      <td>2728207</td>\n",
       "      <td>http://lodlaundromat.org/.well-known/genid/08c...</td>\n",
       "      <td>469396087</td>\n",
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       "    <tr>\n",
       "      <th>1221606</th>\n",
       "      <td>1221606</td>\n",
       "      <td>http://lodlaundromat.org/.well-known/genid/08c...</td>\n",
       "      <td>469396087</td>\n",
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      ],
      "text/plain": [
       "          row_id                                        concept_uri  \\\n",
       "2357292  2357292        http://purl.org/linked-data/cube#Attachable   \n",
       "46156      46156       http://purl.org/linked-data/cube#Observation   \n",
       "1946860  1946860  http://www.w3.org/1999/02/22-rdf-syntax-ns#Res...   \n",
       "2728207  2728207  http://lodlaundromat.org/.well-known/genid/08c...   \n",
       "1221606  1221606  http://lodlaundromat.org/.well-known/genid/08c...   \n",
       "\n",
       "         nb_instances  \n",
       "2357292    1306578332  \n",
       "46156      1306389397  \n",
       "1946860     469782792  \n",
       "2728207     469396087  \n",
       "1221606     469396087  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_inf_sorted =  df_with_inf.sort_values(by='nb_instances', ascending=False)\n",
    "df_with_inf_sorted.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
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       "      <th>nb_instances</th>\n",
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       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>191585</td>\n",
       "      <td>191585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 100]</th>\n",
       "      <td>571369</td>\n",
       "      <td>8916489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>64553</td>\n",
       "      <td>23638829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 100000]</th>\n",
       "      <td>5169</td>\n",
       "      <td>44034831</td>\n",
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       "    <tr>\n",
       "      <th>(100000, 100000000]</th>\n",
       "      <td>551</td>\n",
       "      <td>1188532097</td>\n",
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       "      <th>(100000000, 2000000000]</th>\n",
       "      <td>5</td>\n",
       "      <td>2056040477</td>\n",
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      ],
      "text/plain": [
       "                          count         sum\n",
       "nb_instances                               \n",
       "(0, 1]                   191585      191585\n",
       "(1, 100]                 571369     8916489\n",
       "(100, 1000]               64553    23638829\n",
       "(1000, 100000]             5169    44034831\n",
       "(100000, 100000000]         551  1188532097\n",
       "(100000000, 2000000000]       5  2056040477"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.cut(df_no_inf_sorted['nb_instances'], [0, 1, 100, 1000, 100000, 100000000, 2000000000])\n",
    "df_no_inf_sorted.groupby(bins)['nb_instances'].agg(['count', 'sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>(0, 1]</th>\n",
       "      <td>213852</td>\n",
       "      <td>213852</td>\n",
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       "    <tr>\n",
       "      <th>(1, 100]</th>\n",
       "      <td>646834</td>\n",
       "      <td>10378649</td>\n",
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       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>92041</td>\n",
       "      <td>34419380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 100000]</th>\n",
       "      <td>19058</td>\n",
       "      <td>184548026</td>\n",
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       "    <tr>\n",
       "      <th>(100000, 100000000]</th>\n",
       "      <td>4271</td>\n",
       "      <td>22602059803</td>\n",
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       "    <tr>\n",
       "      <th>(100000000, 2000000000]</th>\n",
       "      <td>618</td>\n",
       "      <td>189766429663</td>\n",
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      ],
      "text/plain": [
       "                          count           sum\n",
       "nb_instances                                 \n",
       "(0, 1]                   213852        213852\n",
       "(1, 100]                 646834      10378649\n",
       "(100, 1000]               92041      34419380\n",
       "(1000, 100000]            19058     184548026\n",
       "(100000, 100000000]        4271   22602059803\n",
       "(100000000, 2000000000]     618  189766429663"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.cut(df_with_inf_sorted['nb_instances'], [0, 1, 100, 1000, 100000, 100000000, 2000000000])\n",
    "df_with_inf_sorted.groupby(bins)['nb_instances'].agg(['count', 'sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>concept_id</th>\n",
       "      <th>concept_uri</th>\n",
       "      <th>nb_instances</th>\n",
       "      <th>inference</th>\n",
       "      <th>comment</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114</td>\n",
       "      <td>http://xmlns.com/foaf/0.1/Person</td>\n",
       "      <td>132919327</td>\n",
       "      <td>(no</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1302</td>\n",
       "      <td>http://geovocab.org/spatial#Feature</td>\n",
       "      <td>30485678</td>\n",
       "      <td>(no</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44</td>\n",
       "      <td>http://purl.org/ontology/bibo/Book</td>\n",
       "      <td>27145467</td>\n",
       "      <td>(no</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>162</td>\n",
       "      <td>http://xmlns.com/foaf/0.1/Document</td>\n",
       "      <td>23789671</td>\n",
       "      <td>(no</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1337</td>\n",
       "      <td>http://www.w3.org/2002/07/owl#Thing</td>\n",
       "      <td>19585983</td>\n",
       "      <td>(no</td>\n",
       "      <td>inf)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    concept_id                          concept_uri  nb_instances inference  \\\n",
       "7          114     http://xmlns.com/foaf/0.1/Person     132919327       (no   \n",
       "9         1302  http://geovocab.org/spatial#Feature      30485678       (no   \n",
       "13          44   http://purl.org/ontology/bibo/Book      27145467       (no   \n",
       "14         162   http://xmlns.com/foaf/0.1/Document      23789671       (no   \n",
       "16        1337  http://www.w3.org/2002/07/owl#Thing      19585983       (no   \n",
       "\n",
       "   comment  \n",
       "7     inf)  \n",
       "9     inf)  \n",
       "13    inf)  \n",
       "14    inf)  \n",
       "16    inf)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mapped_concepts_with_inf = df_mapped_concepts[df_mapped_concepts['inference']=='(with']\n",
    "df_mapped_concepts_no_inf = df_mapped_concepts[df_mapped_concepts['inference']=='(no']\n",
    "\n",
    "df_mapped_concepts_with_inf_sorted =  df_mapped_concepts_with_inf.sort_values(by='nb_instances', ascending=False)\n",
    "df_mapped_concepts_no_inf_sorted =  df_mapped_concepts_no_inf.sort_values(by='nb_instances', ascending=False)\n",
    "\n",
    "df_mapped_concepts_no_inf_sorted.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_instances</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 100]</th>\n",
       "      <td>776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 100000]</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 100000000]</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000000, 2000000000]</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "nb_instances                  \n",
       "(0, 1]                     199\n",
       "(1, 100]                   776\n",
       "(100, 1000]                151\n",
       "(1000, 100000]             146\n",
       "(100000, 100000000]         84\n",
       "(100000000, 2000000000]      1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.cut(df_mapped_concepts_no_inf_sorted['nb_instances'], [0, 1, 100, 1000, 100000, 100000000, 2000000000])\n",
    "df_mapped_concepts_no_inf_sorted.groupby(bins)['nb_instances'].agg(['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nb_instances</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 100]</th>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 100000]</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 100000000]</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000000, 2000000000]</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "nb_instances                  \n",
       "(0, 1]                     178\n",
       "(1, 100]                   753\n",
       "(100, 1000]                161\n",
       "(1000, 100000]             171\n",
       "(100000, 100000000]         87\n",
       "(100000000, 2000000000]      7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.cut(df_mapped_concepts_with_inf_sorted['nb_instances'], [0, 1, 100, 1000, 100000, 100000000, 2000000000])\n",
    "df_mapped_concepts_with_inf_sorted.groupby(bins)['nb_instances'].agg(['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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