5. Knowledge Graph Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "output_dir = 'output/'\n",
    "metadata_file = 'data/metadata.csv'\n",
    "annotations_file = 'data/annotations.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>subject</th>\n",
       "      <th>imprint</th>\n",
       "      <th>sold_by</th>\n",
       "      <th>start_page</th>\n",
       "      <th>end_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9781429219617</td>\n",
       "      <td>BIOLOGY OF PLANTS</td>\n",
       "      <td>PETER H RAVEN</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>FREEMAN/WORTH</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>21</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9781429298643</td>\n",
       "      <td>LIFE: THE SCIENCE OF BIOLOGY</td>\n",
       "      <td>DAVID E SADAVA</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>FREEMAN/WORTH</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>51</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9781464126147</td>\n",
       "      <td>MOLECULAR BIOLOGY: PRINCIPLES AND PRACTICE</td>\n",
       "      <td>MICHAEL M COX</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>W. H. FREEMAN</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>30</td>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9781464135958</td>\n",
       "      <td>WHAT IS LIFE? A GUIDE TO BIOLOGY</td>\n",
       "      <td>JAY PHELAN</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>FREEMAN/WORTH</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>34</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9781429242301</td>\n",
       "      <td>INTRODUCING PSYCHOLOGY</td>\n",
       "      <td>DANIEL L SCHACTER</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>FREEMAN/WORTH</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>38</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9781429298902</td>\n",
       "      <td>PSYCHOLOGY: A CONCISE INTRODUCTION</td>\n",
       "      <td>RICHARD A GRIGGS</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>WORTH PUBLISHERS</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>22</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9781464140815</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>DAVID G MYERS</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>FREEMAN/WORTH</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>59</td>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9781464154072</td>\n",
       "      <td>EXPLORING PSYCHOLOGY</td>\n",
       "      <td>DAVID G MYERS</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>WORTH PUBLISHERS</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>59</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9781464171703</td>\n",
       "      <td>ABNORMAL PSYCHOLOGY</td>\n",
       "      <td>RONALD J COMER</td>\n",
       "      <td>PSYCHOLOGY</td>\n",
       "      <td>WORTH PUBLISHERS</td>\n",
       "      <td>Macmillan Higher Education</td>\n",
       "      <td>33</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ISBN                                       title  \\\n",
       "0  9781429219617                           BIOLOGY OF PLANTS   \n",
       "2  9781429298643                LIFE: THE SCIENCE OF BIOLOGY   \n",
       "4  9781464126147  MOLECULAR BIOLOGY: PRINCIPLES AND PRACTICE   \n",
       "5  9781464135958            WHAT IS LIFE? A GUIDE TO BIOLOGY   \n",
       "1  9781429242301                      INTRODUCING PSYCHOLOGY   \n",
       "3  9781429298902          PSYCHOLOGY: A CONCISE INTRODUCTION   \n",
       "6  9781464140815                                  PSYCHOLOGY   \n",
       "7  9781464154072                        EXPLORING PSYCHOLOGY   \n",
       "8  9781464171703                         ABNORMAL PSYCHOLOGY   \n",
       "\n",
       "              author     subject           imprint  \\\n",
       "0      PETER H RAVEN     BIOLOGY     FREEMAN/WORTH   \n",
       "2     DAVID E SADAVA     BIOLOGY     FREEMAN/WORTH   \n",
       "4      MICHAEL M COX     BIOLOGY     W. H. FREEMAN   \n",
       "5         JAY PHELAN     BIOLOGY     FREEMAN/WORTH   \n",
       "1  DANIEL L SCHACTER  PSYCHOLOGY     FREEMAN/WORTH   \n",
       "3   RICHARD A GRIGGS  PSYCHOLOGY  WORTH PUBLISHERS   \n",
       "6      DAVID G MYERS  PSYCHOLOGY     FREEMAN/WORTH   \n",
       "7      DAVID G MYERS  PSYCHOLOGY  WORTH PUBLISHERS   \n",
       "8     RONALD J COMER  PSYCHOLOGY  WORTH PUBLISHERS   \n",
       "\n",
       "                      sold_by  start_page  end_page  \n",
       "0  Macmillan Higher Education          21       747  \n",
       "2  Macmillan Higher Education          51      1297  \n",
       "4  Macmillan Higher Education          30       828  \n",
       "5  Macmillan Higher Education          34       718  \n",
       "1  Macmillan Higher Education          38       526  \n",
       "3  Macmillan Higher Education          22       464  \n",
       "6  Macmillan Higher Education          59       751  \n",
       "7  Macmillan Higher Education          59       662  \n",
       "8  Macmillan Higher Education          33       699  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata = pd.read_csv(metadata_file, dtype = {'ISBN': 'str'})\n",
    "metadata = metadata.sort_values('subject')\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_spotlight_concept(annotation):\n",
    "    return annotation['URI'].split('/resource/')[-1]\n",
    "\n",
    "def extract_spotlight_concept_and_location(annotation):\n",
    "    return annotation['URI'].split('/resource/')[-1], annotation['offset']\n",
    "\n",
    "isbns = metadata['ISBN']\n",
    "\n",
    "with open(annotations_file, 'rb') as fp:\n",
    "    annotations = pickle.load(fp) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'URI': 'http://dbpedia.org/resource/Nymphaeaceae',\n",
       " 'offset': 142,\n",
       " 'percentageOfSecondRank': 0.0008415212982582426,\n",
       " 'similarityScore': 0.9991487251046055,\n",
       " 'support': 277,\n",
       " 'surfaceForm': 'water lily',\n",
       " 'types': 'DBpedia:Species,DBpedia:Eukaryote,DBpedia:Plant'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotations[isbns[0]][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract concepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "concepts_df = pd.DataFrame()\n",
    "for isbn in isbns:\n",
    "    concepts = [extract_spotlight_concept(ann) for ann in annotations[isbn]]\n",
    "    freq = Counter(concepts)\n",
    "    concepts, counts = zip(*freq.items())\n",
    "\n",
    "    concepts_df = concepts_df.append(\n",
    "        pd.DataFrame({\n",
    "                      'concept': list(concepts),\n",
    "                      'count': list(counts),\n",
    "                      'ISBN': isbn\n",
    "                    }))         \n",
    "\n",
    "concepts_df = concepts_df.sort_values('count', ascending=False)\n",
    "concepts_df = pd.merge(concepts_df, metadata, on='ISBN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Concepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 concepts for: BIOLOGY OF PLANTS\n",
      "Meristem\n",
      "Protein\n",
      "Xylem\n",
      "DNA\n",
      "Micrometre\n",
      "Fungus\n",
      "Chloroplast\n",
      "Gametophyte\n",
      "Flowering_plant\n",
      "Phloem\n",
      "\n",
      "\n",
      "Top 10 concepts for: LIFE: THE SCIENCE OF BIOLOGY\n",
      "Protein\n",
      "DNA\n",
      "Enzyme\n",
      "Gene\n",
      "Oxygen\n",
      "Adenosine_triphosphate\n",
      "Bacteria\n",
      "Amino_acid\n",
      "Eukaryote\n",
      "Cell_membrane\n",
      "\n",
      "\n",
      "Top 10 concepts for: MOLECULAR BIOLOGY: PRINCIPLES AND PRACTICE\n",
      "DNA\n",
      "Protein\n",
      "Messenger_RNA\n",
      "RNA\n",
      "Transcription_(genetics)\n",
      "Eukaryote\n",
      "Gene\n",
      "Enzyme\n",
      "Transfer_RNA\n",
      "Nucleotide\n",
      "\n",
      "\n",
      "Top 10 concepts for: WHAT IS LIFE? A GUIDE TO BIOLOGY\n",
      "DNA\n",
      "Allele\n",
      "Bacteria\n",
      "Protein\n",
      "Free_On-line_Dictionary_of_Computing/E_-_H\n",
      "Ampere\n",
      "Chromosome\n",
      "Gene\n",
      "Eukaryote\n",
      "Natural_selection\n",
      "\n",
      "\n",
      "Top 10 concepts for: INTRODUCING PSYCHOLOGY\n",
      "Neuron\n",
      "Psychology\n",
      "Anxiety\n",
      "Psychotherapy\n",
      "Classical_conditioning\n",
      "Neurotransmitter\n",
      "Axon\n",
      "Schizophrenia\n",
      "Retina\n",
      "Amygdala\n",
      "\n",
      "\n",
      "Top 10 concepts for: PSYCHOLOGY: A CONCISE INTRODUCTION\n",
      "Neuron\n",
      "Classical_conditioning\n",
      "Schizophrenia\n",
      "Operant_conditioning\n",
      "Long-term_memory\n",
      "Retina\n",
      "Heuristic\n",
      "Anxiety\n",
      "Major_depressive_disorder\n",
      "Placebo\n",
      "\n",
      "\n",
      "Top 10 concepts for: PSYCHOLOGY\n",
      "Psychology\n",
      "Anxiety\n",
      "Schizophrenia\n",
      "Major_depressive_disorder\n",
      "Depression_(mood)\n",
      "Neuron\n",
      "Hormone\n",
      "Happiness\n",
      "Psychotherapy\n",
      "Neurotransmitter\n",
      "\n",
      "\n",
      "Top 10 concepts for: EXPLORING PSYCHOLOGY\n",
      "Information_technology\n",
      "Psychology\n",
      "Anxiety\n",
      "Major_depressive_disorder\n",
      "Depression_(mood)\n",
      "Schizophrenia\n",
      "Neuron\n",
      "Self-esteem\n",
      "Hormone\n",
      "Test_(assessment)\n",
      "\n",
      "\n",
      "Top 10 concepts for: ABNORMAL PSYCHOLOGY\n",
      "Schizophrenia\n",
      "Major_depressive_disorder\n",
      "Anxiety\n",
      "United_States\n",
      "Depression_(mood)\n",
      "Mental_disorder\n",
      "Personality_disorder\n",
      "Diagnostic_and_Statistical_Manual_of_Mental_Disorders\n",
      "Neurotransmitter\n",
      "Psychodynamics\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for isbn in isbns:\n",
    "    print('Top 10 concepts for: {}'.format(metadata.loc[metadata['ISBN']==isbn, 'title'].values[0]))    \n",
    "    isbn_concepts = concepts_df[concepts_df['ISBN'] == isbn]\n",
    "    top_concepts = isbn_concepts.head(10)['concept']              \n",
    "    print('\\n'.join(top_concepts))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Textbook correlation heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_matrix = concepts_df.pivot_table(index='concept', \n",
    "                                       columns='title',\n",
    "                                       values='count',\n",
    "                                       fill_value=0)\n",
    "count_matrix = count_matrix[metadata['title']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix = count_matrix.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TmM+V1dlatu2xwD2kqchDc6AXQgjdQwfmcEkaA6xDSlE5yPZfC+s+R5pl6As8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe904a139e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "ax = sns.heatmap(corr_matrix, cmap='coolwarm')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}