From 38cb2134e72760c64bb069fabd9f55fb6f23d5ea Mon Sep 17 00:00:00 2001 From: angelosalatino Date: Fri, 3 May 2019 13:09:50 +0100 Subject: [PATCH] releasing version 2 --- CSO-Classifier.ipynb | 151 + CSO-Classifier.py | 83 + README.md | 221 +- classifier/misc.py | 500 + classifier/models/README.md | 27 + .../models/cso.csv | 0 classifier/models/cso.p | Bin 0 -> 4538269 bytes classifier/semanticmodule.py | 383 + classifier/syntacticmodule.py | 207 + example.py | 71 - images/Workflow.png | Bin 0 -> 183691 bytes ontology/ComputerScienceOntology.csv | 95414 ---------------- pics/framework.png | Bin 137297 -> 0 bytes requirements.txt | 111 +- skm3.py | 495 - 15 files changed, 1570 insertions(+), 96093 deletions(-) create mode 100644 CSO-Classifier.ipynb create mode 100644 CSO-Classifier.py create mode 100644 classifier/misc.py create mode 100644 classifier/models/README.md rename ontology/ComputerScienceOntology_v2.csv => classifier/models/cso.csv (100%) create mode 100644 classifier/models/cso.p create mode 100644 classifier/semanticmodule.py create mode 100644 classifier/syntacticmodule.py delete mode 100644 example.py create mode 100644 images/Workflow.png delete mode 100644 ontology/ComputerScienceOntology.csv delete mode 100644 pics/framework.png delete mode 100644 skm3.py diff --git a/CSO-Classifier.ipynb b/CSO-Classifier.ipynb new file mode 100644 index 0000000..918d023 --- /dev/null +++ b/CSO-Classifier.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import classifier.misc as misc\n", + "from classifier.syntacticmodule import CSOClassifierSyntactic as synt\n", + "from classifier.semanticmodule import CSOClassifierSemantic as sema\n", + "\n", + "import json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Paper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "paper = {\n", + " \"title\": \"De-anonymizing Social Networks\",\n", + " \"abstract\": \"Operators of online social networks are increasingly sharing potentially \"\n", + " \"sensitive information about users and their relationships with advertisers, application \"\n", + " \"developers, and data-mining researchers. Privacy is typically protected by anonymization, \"\n", + " \"i.e., removing names, addresses, etc. We present a framework for analyzing privacy and \"\n", + " \"anonymity in social networks and develop a new re-identification algorithm targeting \"\n", + " \"anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, \"\n", + " \"we show that a third of the users who can be verified to have accounts on both Twitter, a \"\n", + " \"popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified \"\n", + " \"in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is \"\n", + " \"based purely on the network topology, does not require creation of a large number of dummy \"\n", + " \"\\\"sybil\\\" nodes, is robust to noise and all existing defenses, and works even when the overlap \"\n", + " \"between the target network and the adversary's auxiliary information is small.\",\n", + " \"keywords\": \"data mining, data privacy, graph theory, social networking (online)\"\n", + " }\n", + "\n", + " \n", + "\n", + "from IPython.display import display, HTML\n", + "\n", + "display(HTML('

'+paper[\"title\"]+'

'))\n", + "display(HTML('

Abstract: '+paper[\"abstract\"]+'

'))\n", + "display(HTML('

Keywords: '+paper[\"keywords\"]+'

'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Model, CSO and initialize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cso, model = misc.load_ontology_and_model()\n", + "\n", + "# Passing parematers to the two classes (synt and sema)\n", + "synt_module = synt(cso, paper)\n", + "sema_module = sema(model, cso, paper)\n", + "\n", + "#initializing variable that will contain output\n", + "class_res = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class_res[\"syntactic\"] = synt_module.classify_syntactic()\n", + "class_res[\"semantic\"] = sema_module.classify_semantic()\n", + "\n", + "union = list(set(class_res[\"syntactic\"] + class_res[\"semantic\"]))\n", + "class_res[\"union\"] = union\n", + "\n", + "enhanced = misc.climb_ontology(cso,union)\n", + "class_res[\"enhanced\"] = [x for x in enhanced if x not in union]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Printing and Saving" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(class_res)\n", + "\n", + "with open('output.json', 'w') as outfile:\n", + " json.dump(class_res, outfile, indent=4)" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/CSO-Classifier.py b/CSO-Classifier.py new file mode 100644 index 0000000..09f133b --- /dev/null +++ b/CSO-Classifier.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 14 14:43:42 2019 + +@author: angelosalatino +""" + + + +# In[Loading Libraries]: + + +import classifier.misc as misc +from classifier.syntacticmodule import CSOClassifierSyntactic as synt +from classifier.semanticmodule import CSOClassifierSemantic as sema + +import json + + +# In[Loading Paper]: + + +paper = { + "title": "De-anonymizing Social Networks", + "abstract": "Operators of online social networks are increasingly sharing potentially " + "sensitive information about users and their relationships with advertisers, application " + "developers, and data-mining researchers. Privacy is typically protected by anonymization, " + "i.e., removing names, addresses, etc. We present a framework for analyzing privacy and " + "anonymity in social networks and develop a new re-identification algorithm targeting " + "anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, " + "we show that a third of the users who can be verified to have accounts on both Twitter, a " + "popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified " + "in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is " + "based purely on the network topology, does not require creation of a large number of dummy " + "\"sybil\" nodes, is robust to noise and all existing defenses, and works even when the overlap " + "between the target network and the adversary's auxiliary information is small.", + "keywords": "data mining, data privacy, graph theory, social networking (online)" + } + + +print(paper["title"]) +print(paper["abstract"]) +print(paper["keywords"]) + + + +# In[Load Model, CSO and initialize]: + + +cso, model = misc.load_ontology_and_model() + +# Passing parematers to the two classes (synt and sema) +synt_module = synt(cso, paper) +sema_module = sema(model, cso, paper) + +#initializing variable that will contain output +class_res = {} + + + +# In[Running]: + + +class_res["syntactic"] = synt_module.classify_syntactic() +class_res["semantic"] = sema_module.classify_semantic() + +union = list(set(class_res["syntactic"] + class_res["semantic"])) +class_res["union"] = union + +enhanced = misc.climb_ontology(cso,union) +class_res["enhanced"] = [x for x in enhanced if x not in union] + + + +# In[Printing and Saving]: + + +print(class_res) + +with open('output.json', 'w') as outfile: + json.dump(class_res, outfile, indent=4) + diff --git a/README.md b/README.md index 179b3d5..97b062d 100644 --- a/README.md +++ b/README.md @@ -1,132 +1,131 @@ # CSO-Classifier -Script that classifes content from scientific papers with the topics of the [Computer Science Ontology (CSO)](https://cso.kmi.open.ac.uk). Being able to synthesize the content of papers, allows to perform different kinds of analytics: -* Trend analysis -* Recommender systems -* Find authors’ topics of interest -* Topic analysis +Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this repository, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the [Computer Science Ontology (CSO)](https://cso.kmi.open.ac.uk), a comprehensive ontology of research areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods. ## About -If you use the CSO classfier in your research or work and would like to cite the SKM3 Application Programming Interface, we suggest you cite the [CSO portal paper](http://skm.kmi.open.ac.uk/the-computer-science-ontology-a-large-scale-taxonomy-of-research-areas/). +The CSO Classifier is a novel application that takes as input the text from abstract, title, and keywords of a research paper and outputs a list of relevant concepts from CSO. It consists of two main components: (i) the syntactic module and (ii) the semantic module. Figure 1 depicts its architecture. The syntactic module parses the input documents and identifies CSO concepts that are explicitly referred in the document. The semantic module uses part-of-speech tagging to identify promising terms and then exploits word embeddings to infer semantically related topics. Finally, the CSO Classifier combines the results of these two modules and enhances them by including relevant super-areas. -## Framework -![Framework of CSO Classifier](/pics/framework.png "Framework of CSO Classifier") +![Framework of CSO Classifier](/images/Workflow.png "Framework of CSO Classifier") ## Requirements 1. Ensure you have [**Python 3**](https://www.python.org/downloads/) installed. 2. Install the necessary depepencies by executing the following command:```pip install -r requirements.txt``` -3. Download NLP datasets by running the following line within your Python 3 interpreter: ```import nltk; nltk.download('stopwords');``` +3. Download English package for spaCy using ```python -m spacy download en_core_web_sm``` +4. Download the word2vec model. The current model has a size of 350MB and cannot be stored in Github. For this reason it must be downloaded separately from [https://cso.kmi.open.ac.uk/download/model.p](https://cso.kmi.open.ac.uk/download/model.p) and stored in the *models* folder: ```/classifier/models/``` -## In depth -1. The algorithm firstly preprocesses the content of each paper: removes punctuation and stop words. -2. Then, it parses the text to find n-grams (unigram, bigrams and trigrams) that match, with a certain degree of similarity (default: Levenshtein >= 0.85), with the topics within the Computer Science Ontology. -3. Thirdly, it adds more broader generic topics, based on the ones retrieved in Step 2. It exploits the _skos:broaderGeneric_ relationships within the CSO. A more broader topic is included if a certain amount of narrower topics (default: num_narrower = 2) are in the initial set of topics. The selcgtion of more broader generic topics can be achieved in two ways: - * select just the first broader topic, or in other words the direct broaders of the topics extracted from the paper; - * select the whole tree from the first broader topic up until the root of the ontology. -4. Lastly, it cleans the output removing statistic values, and removes similar topics using the _relatedEquivalent_ within the CSO. +## Main Files +* **CSO-Classifier.ipynb**: :page_facing_up: Python notebook for executing the classifier +* **requirements.txt**: :page_facing_up: File containing the necessary libraries to run the classifier +* **images**: :file_folder: folder containing some pictures, e.g., the workflow showed above +* **classifier**: :file_folder: Folder containing the main functionalities of the classifier + - **syntacticmodule.py**: :page_facing_up: functionalities that implement the syntactic module + - **semanticmodule.py**: :page_facing_up: functionalities that implement the semantic module + - **misc.py**: :page_facing_up: some miscellaneous functionalities + - **models**: :file_folder: Folder containing the word2vec model and CSO + - **cso.p**: :page_facing_up: file containing the Computer Science Ontology + - **model.p**: :page_facing_up: file containing the word2vec model. Please be aware that this file is not currently tracked within this repository due to its large size (over 100MB). This file needs to be downloaded separately (see ```Requirements #4``` above). -## Choosing the Ontology -In the repository you can find two versions of the CSO (_ComputerScienceOntology.csv_): -```python -# Version 1: 15K topics and 90K relationships -clf = CSO(version=1) -``` -or -```python -# Version 2: 26K topics and 226K relationships -clf = CSO(version=2) -``` - - -## Instance -Input: +## Sample Input ```json paper = { - "title": "Detection of Embryonic Research Topics by Analysing Semantic Topic Networks", - "abstract": "Being aware of new research topics is an important asset for anybody involved in the research environment, including researchers, academic publishers and institutional funding bodies. In recent years, the amount of scholarly data available on the web has increased steadily, allowing the development of several approaches for detecting emerging research topics and assessing their trends. However, current methods focus on the detection of topics which are already associated with a label or a substantial number of documents. In this paper, we address instead the issue of detecting embryonic topics, which do not possess these characteristics yet. We suggest that it is possible to forecast the emergence of novel research topics even at such early stage and demonstrate that the emergence of a new topic can be anticipated by analysing the dynamics of pre-existing topics. We present an approach to evaluate such dynamics and an experiment on a sample of 3 million research papers, which confirms our hypothesis. In particular, we found that the pace of collaboration in sub-graphs of topics that will give rise to novel topics is significantly higher than the one in the control group.", - "keywords": "Scholarly Data, Research Trend Detection, Topic Emergence Detection, Topic Discovery, Semantic Web, Ontology" + "title": "De-anonymizing Social Networks", + "abstract": "Operators of online social networks are increasingly sharing potentially " + "sensitive information about users and their relationships with advertisers, application " + "developers, and data-mining researchers. Privacy is typically protected by anonymization, " + "i.e., removing names, addresses, etc. We present a framework for analyzing privacy and " + "anonymity in social networks and develop a new re-identification algorithm targeting " + "anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, " + "we show that a third of the users who can be verified to have accounts on both Twitter, a " + "popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified " + "in the anonymous Twitter graph with only a 12% error rate. Our de-anonymization algorithm is " + "based purely on the network topology, does not require creation of a large number of dummy " + "\"sybil\" nodes, is robust to noise and all existing defenses, and works even when the overlap " + "between the target network and the adversary's auxiliary information is small.", + "keywords": "data mining, data privacy, graph theory, social networking (online)" } ``` -Running the classifier: -```python -# cso is a dictionary loaded beforehand -# num_narrower = 1, include all the broader topics having at least one narrower topic matched in the paper -# min_similarity = 0.9, more precise similarity between n-grams and topics has been requested -# climb_ont = 'jfb', it adds 'just the first broader topic'. The other option available is 'wt' as it adds the whole tree up until the root. -# verbose = True, it returns the result in a verbose way. It reports the different statistics associated with matches. -result = clf.classify(PAPER, format='json', num_narrower=1, min_similarity=0.9, climb_ont='jfb', verbose=True) -print(json.dumps(result)) -``` -Result (variable **_result_**): -```json -{ - "extracted":{ - "semantics":[ - { - "matched":"semantic", - "similarity":0.9411764705882353 - }, - { - "matched":"semantic", - "similarity":0.9411764705882353 - } - ], - "ontology":[ - { - "matched":"ontology", - "similarity":1.0 - } - ], - "semantic web":[ - { - "matched":"semantic web", - "similarity":1.0 - } - ] - }, - "inferred":{ - "semantics":[ - { - "matched":2, - "broader of":[ - "ontology", - "semantic web" - ] - } - ], - "world wide web":[ - { - "matched":1, - "broader of":[ - "semantic web" - ] - } - ] - } -} -``` -Within the key _extracted_ you can find the syntactic match between topics and portion of theanalysed text. Whilst, within the _inferred_ key you can find the semantically inferred topics. - -If you want a more cleaned result, you can run the same function with _verbose=False_: -```python -result = clf.classify(PAPER, format='json', num_narrower=1, min_similarity=0.9, climb_ont='jfp', verbose=False) -print(json.dumps(result)) -``` - -List of final topics (variable **_result_**): +## Sample Output ```json -{ - "extracted":[ - "ontology", - "semantics", - "semantic web" - ], - "inferred":[ - "world wide web" - ] +{ + "syntactic": [ + "twitter", + "graph theory", + "data mining", + "anonymization", + "online social networks", + "data privacy", + "network topology", + "anonymity", + "sensitive informations", + "microblogging", + "social networks", + "privacy", + "real-world networks" + ], + "semantic": [ + "social networks", + "online social networks", + "sensitive informations", + "data mining", + "privacy", + "data privacy", + "anonymization", + "anonymity", + "network topology", + "twitter", + "microblogging", + "topology", + "graph theory", + "social media", + "social networking sites", + "network structures", + "network architecture", + "micro-blog", + "online communities", + "social graphs" + ], + "union": [ + "network architecture", + "data privacy", + "network topology", + "graph theory", + "micro-blog", + "network structures", + "social graphs", + "microblogging", + "topology", + "twitter", + "social networks", + "social media", + "data mining", + "online social networks", + "privacy", + "social networking sites", + "anonymization", + "anonymity", + "sensitive informations", + "real-world networks", + "online communities" + ], + "enhanced": [ + "privacy preserving", + "complex networks", + "online systems", + "facebook", + "computer science", + "access control", + "neural networks", + "electric network topology", + "world wide web", + "network security", + "security of data", + "authentication", + "network protocols", + "theoretical computer science" + ] } -``` +``` \ No newline at end of file diff --git a/classifier/misc.py b/classifier/misc.py new file mode 100644 index 0000000..1fcb7bf --- /dev/null +++ b/classifier/misc.py @@ -0,0 +1,500 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Nov 22 08:45:29 2018 + +@author: angelosalatino + +This file mostly contains routines to deal with the Computer Science Ontology and the model. +""" + +import pickle +import os +import sys +import requests +import networkx as nx +from webweb import Web +import numpy as np +from hurry.filesize import size +import csv as co + +#some global variables +CSO_PATH = "classifier/models/cso.csv" +CSO_PICKLE_PATH = "classifier/models/cso.p" +MODEL_PICKLE_PATH = "classifier/models/model.p" +MODEL_PICKLE_REMOTE_URL = "https://cso.kmi.open.ac.uk/download/model.p" + + + +def load_cso(): + """Function that loads the CSO from the file in a dictionary. + In particular, it load all the relationships organised in boxes: + - topics, the list of topics + - broaders, the list of broader topics for a given topic + - narrowers, the list of narrower topics for a given topic + - same_as, all the siblings for a given topic + - primary_labels, all the primary labels of topics, if they belong to clusters + - topics_wu, topic with underscores + - primary_labels_wu, primary labels with underscores + + Args: + + + Returns: + cso (dictionary): {'topics':topics, 'broaders':broaders, 'narrowers':narrowers, 'same_as':same_as, 'primary_labels': primary_labels}. + """ + + with open(CSO_PATH, 'r') as ontoFile: + topics = {} + topics_wu = {} + broaders = {} + narrowers = {} + same_as = {} + primary_labels = {} + primary_labels_wu = {} + ontology = co.reader(ontoFile, delimiter=';') + + for triple in ontology: + if triple[1] == 'klink:broaderGeneric': + # loading broader topics + if triple[2] in broaders: + broaders[triple[2]].append(triple[0]) + else: + broaders[triple[2]] = [triple[0]] + + # loading narrower topics + if triple[0] in narrowers: + narrowers[triple[0]].append(triple[2]) + else: + narrowers[triple[0]] = [triple[2]] + elif triple[1] == 'klink:relatedEquivalent': + if triple[2] in same_as: + same_as[triple[2]].append(triple[0]) + else: + same_as[triple[2]] = [triple[0]] + elif triple[1] == 'rdfs:label': + topics[triple[0]] = True + topics_wu[triple[0].replace(" ", "_")] = triple[0] + elif triple[1] == 'klink:primaryLabel': + primary_labels[triple[0]] = triple[2] + primary_labels_wu[triple[0].replace(" ", "_")] = triple[2].replace(" ", "_") + + cso = { + 'topics': topics, + 'broaders': broaders, + 'narrowers': narrowers, + 'same_as': same_as, + 'primary_labels': primary_labels, + 'topics_wu': topics_wu, + 'primary_labels_wu': primary_labels_wu + } + + + + return cso + + + +def load_ontology_pickle(): + """Function that loads CSO. + This file has been serialised using Pickle allowing to be loaded quickly. + + Args: + + Returns: + fcso (dictionary): containing the found topics with their similarity and the n-gram analysed. + """ + check_ontology() + fcso = pickle.load( open( CSO_PICKLE_PATH, "rb" ) ) + return fcso + + +def load_ontology_and_model(): + """Function that loads both CSO and Word2vec model. + Those two files have been serialised using Pickle allowing to be loaded quickly. + + + Args: + + Returns: + fcso (dictionary): containing the found topics with their similarity and the n-gram analysed. + fmodel (dictionary): containing the found topics with their similarity and the n-gram analysed. + """ + + check_ontology() + check_model() + + fcso = pickle.load( open( CSO_PICKLE_PATH, "rb" ) ) + fmodel = pickle.load( open( MODEL_PICKLE_PATH, "rb" ) ) + + print("Computer Science Ontology and Word2vec model loaded.") + return fcso, fmodel + + +def check_ontology(): + """Function that checks if the ontology is available. If not, it will check if a csv version exists and then it will create the pickle file. + + """ + + if not os.path.exists(CSO_PICKLE_PATH): + print("Ontology pickle file is missing.") + cso = load_cso() + with open(CSO_PICKLE_PATH, 'wb') as cso_file: + print("Creating ontology pickle file from a copy of the CSO Ontology found in",CSO_PATH) + pickle.dump(cso, cso_file) + + +def check_model(): + """Function that checks if the model is available. If not, it will attempt to download it from a remote location. + Tipically hosted on the CSO Portal + + """ + + if not os.path.exists(MODEL_PICKLE_PATH): + print('[*] Beginning model download from', MODEL_PICKLE_REMOTE_URL) + download_file(MODEL_PICKLE_REMOTE_URL, MODEL_PICKLE_PATH) + + +def download_file(url, filename): + """Function that downloads the model from the web. + + Args: + url (string): Url of where the model is located. + filename (string): location of where to save the model + + Returns: + + """ + with open(filename, 'wb') as f: + response = requests.get(url, stream=True) + total = response.headers.get('content-length') + + if total is None: + f.write(response.content) + else: + downloaded = 0 + total = int(total) + for data in response.iter_content(chunk_size=max(int(total/1000), 1024*1024)): + downloaded += len(data) + f.write(data) + done = int(50*downloaded/total) + sys.stdout.write('\r[{}{}] {}/{}'.format('█' * done, '.' * (50-done), size(downloaded), size(total))) + sys.stdout.flush() + sys.stdout.write('\n') + print('[*] Done!') + + +def get_primary_label(topic, primary_labels): + """Function that returns the primary (preferred) label for a topic. If this topic belongs to + a cluster. + + Args: + topic (string): Topic to analyse. + primary_labels (dictionary): It contains the primary labels of all the topics belonging to clusters. + + Returns: + topic (string): primary label of the analysed topic. + """ + + try: + topic = primary_labels[topic] + except KeyError: + pass + + return topic + + +def climb_ontology(cso, found_topics): + """Function that climbs the ontology. This function might retrieve + just the first broader topic or the whole branch up until root + + Args: + found_topics (dictionary): It contains the topics found with string similarity. + cso (dictionary): the ontology previously loaded from the file. + num_narrower (integer): it defines the number of narrower topics before their broader topic gets included + in the final set of topics. Default = 2. + climb_ont (string): either "jfb" or "wt" for selecting "just the first broader topic" or climbing + the "whole tree". + + Returns: + found_topics (dictionary): containing the found topics with their similarity and the n-gram analysed. + """ + + all_broaders = {} + inferred_topics = {} + num_narrower = 1 + all_broaders = get_broader_of_topics(cso, found_topics, all_broaders) + + + + for broader, narrower in all_broaders.items(): + if len(narrower) >= num_narrower: + broader = get_primary_label(broader, cso['primary_labels']) + if broader not in inferred_topics: + inferred_topics[broader] = [{'matched': len(narrower), 'broader of': narrower}] + else: + inferred_topics[broader].append({'matched': len(narrower), 'broader of': narrower}) + + return list(set(inferred_topics.keys()).difference(found_topics)) + + + +def get_broader_of_topics(cso, found_topics, all_broaders): + """Function that returns all the broader topics for a given set of topics. + It analyses the broader topics of both the topics initially found in the paper, and the broader topics + found at the previous iteration. + It incrementally provides a more comprehensive set of broader topics. + + Args: + found_topics (dictionary): It contains the topics found with string similarity. + all_broaders (dictionary): It contains the broader topics found in the previous run. + Otherwise an empty object. + cso (dictionary): the ontology previously loaded from the file. + + Returns: + all_broaders (dictionary): contains all the broaders found so far, including the previous iterations. + """ + + topics = list(found_topics) + for topic in topics: + if topic in cso['broaders']: + broaders = cso['broaders'][topic] + for broader in broaders: + if broader in all_broaders: + if topic not in all_broaders[broader]: + all_broaders[broader].append(topic) + else: + pass # the topic was listed before + else: + all_broaders[broader] = [topic] + + return all_broaders + + +def get_network(cso, found_topics): + """Function that extracts the network from a given set of topics. + Args: + found_topics (list): It contains the list of identified topics. + cso (dictionary): the ontology previously loaded from the file. + + Returns: + network (dictionary): = {"nodes":nodes, "edges":edges} contains the list of nodes and edges of the extracetd network. + """ + + if type(found_topics) is dict: + list_of_topics = [] + for key, value in found_topics.items(): + list_of_topics += value + + list_of_topics = list(set(list_of_topics)) + elif type(found_topics) is list: + list_of_topics = found_topics + + from collections import deque + topics = [] + for topic in list_of_topics: + if topic in cso["topics"]: + topics.append(topic) + else: + print("Asked to process '",topic,"', but I couldn't find it in the current version of the Ontology") + + nodes = [] + edges = [] + + + nodes.append({"id":"paper", "label":"paper"}) + t_id = 0 + pos = {} + for topic in topics: + pos[topic] = t_id + pos[t_id] = topic + temp={"id":"topic"+str(t_id), "label":topic} + nodes.append(temp) + t_id += 1 + + + matrix = np.ones((len(topics),len(topics)), dtype=int)*999 + queue = deque() + for topic in topics: + queue.append({"t":topic,"d":1}) + while len(queue) > 0: + dequeued = queue.popleft() + if dequeued["t"] in cso["broaders"]: + broaders = cso["broaders"][dequeued["t"]] + for broader in broaders: + if broader in pos: + matrix[pos[topic]][pos[broader]] = dequeued["d"] + queue.append({"t":broader,"d":dequeued["d"]+1}) + + + for topic in topics: + nearest_min = matrix[pos[topic]].min() + nearest_pos = np.where(matrix[pos[topic]] == nearest_min)[0] + + if(nearest_min == 1): + for near in nearest_pos: + edge = {"id":"edge","source":topic,"target":pos[near],"kind":"hard"} + edges.append(edge) + elif(nearest_min > 1 and nearest_min < 999): + for near in nearest_pos: + edge = {"id":"edge","source":topic,"target":pos[near],"kind":"soft"} + edges.append(edge) + else: + edge = {"id":"edge","source":topic,"target":"paper","kind":"conn"} + edges.append(edge) + + + + network = {"nodes":nodes, "edges":edges} + return network + + +def plot_network(network): + """Function that plots the network of topics. + It mainly relies on networkx + + Args: + network (dictionary): computed network. + + + Returns: + + """ + + G = nx.DiGraph() + labels={} + for node in network["nodes"]: + G.add_node(node["label"]) + labels[node["label"]] = r'$'+node["label"]+'$' + + + for edge in network["edges"]: + G.add_edge(edge["source"],edge["target"],kind=edge["kind"]) + + + + pos = nx.spring_layout(G) + + + + hard=[(u,v) for (u,v,d) in G.edges(data=True) if d['kind'] == "hard"] + soft=[(u,v) for (u,v,d) in G.edges(data=True) if d['kind'] == "soft"] + conn=[(u,v) for (u,v,d) in G.edges(data=True) if d['kind'] == "conn"] + + + remain = [i for i in G.nodes() if i!="paper"] + nx.draw_networkx_nodes(G,pos, nodelist=["paper"], node_color='orange',node_shape = 's', node_size=500, alpha=1) + nx.draw_networkx_nodes(G,pos, nodelist=remain, node_color='#167096',node_shape = 'o', node_size=100, alpha=1) + + + + nx.draw_networkx_edges(G,pos,edgelist=hard,width=1) + nx.draw_networkx_edges(G,pos,edgelist=soft,width=1,alpha=0.5,style='dashed') + nx.draw_networkx_edges(G,pos,edgelist=conn,width=1,edge_color='black') + + nx.draw_networkx_labels(G,pos,font_size=9,font_family='sans-serif') + + import matplotlib.pyplot as plt + plt.axis('off') + plt.show() + + + +def plot_network2(network): + """Function that plots the network of topics. + It mainly relies on webweb: https://github.com/dblarremore/webweb + + Args: + network (dictionary): computed network. + + + Returns: + + """ + + G = nx.Graph() + for node in network["nodes"]: + G.add_node(node["label"]) + G.nodes[node["label"]]['isPaper'] = False + + G.nodes["paper"]['isPaper'] = True + + + for edge in network["edges"]: + G.add_edge(edge["source"],edge["target"],kind=edge["kind"],style='dashed') + + + # create the web + web = Web(nx_G=G) + + web.display.showNodeNames = True + web.display.colorBy = 'isPaper' + + web.show() + +def get_coverage(cso, found_topics): + """Function that for a given topics, it returns its coverage. + This coverage is computed based on how many its descendants have been identified. + + Args: + found_topics (list): It contains the list of identified topics. + cso (dictionary): the ontology previously loaded from the file. + + Returns: + coverage (dictionary): = {"topic":percentage value} contains all found topics with their percentage of coverage. + """ + + coverage = {} + + if type(found_topics) is dict: + list_of_topics = [] + for key, value in found_topics.items(): + list_of_topics += value + + list_of_topics = list(set(list_of_topics)) + elif type(found_topics) is list: + list_of_topics = found_topics + + + t_id = 0 + pos = {} + topics = [] + for topic in list_of_topics: + if topic in cso["topics"]: + topics.append(topic) + pos[topic] = t_id + pos[t_id] = topic + t_id += 1 + else: + print("Asked to process '",topic,"', but I couldn't find it in the current version of the Ontology") + + matrix = np.zeros((len(topics),len(topics)), dtype=int) + np.fill_diagonal(matrix, 1) + + from collections import deque + queue = deque() + for topic in topics: + queue.append(topic) + while len(queue) > 0: + dequeued = queue.popleft() + if dequeued in cso["broaders"]: + broaders = cso["broaders"][dequeued] + for broader in broaders: + if broader in pos: + matrix[pos[topic]][pos[broader]] = 1#dequeued["d"] + queue.append(broader) + + + + dividend = len(topics) #or np.sum(matrix) + + if(dividend > 0): + general_coverage = np.sum(matrix,axis=0) + + for topic in topics: + coverage[topic] = round(general_coverage[pos[topic]]/dividend,3) + else: + print("I was about to perform a divide by zero operation") + + return coverage + \ No newline at end of file diff --git a/classifier/models/README.md b/classifier/models/README.md new file mode 100644 index 0000000..b8292dc --- /dev/null +++ b/classifier/models/README.md @@ -0,0 +1,27 @@ +# Model Folder + +This folder should contain three files: +* **cso.csv** +* **cso.p** +* **model.p** + +The last two files contain a serialized Python object, created using [Pickle](https://docs.python.org/3/library/pickle.html). Serializing such objects allows us to quickly import them in our workspace. + +## cso.csv +This file contains the Computer Science Ontology describing the relationships between different research concepts. Each row contains a triple (subject, predicate, object). + +## cso.p +This serialized file contains the Computer Science Ontology. In particular, it contains a dictionary with all the relevant information about the different concepts included in CSO. It is produced from the file *cso.csv*. + +## model.p +This serialized file contains the word2vec model that has been trained using the Gensim library: + +``` +gensim.models.keyedvectors.Word2VecKeyedVectors +``` + +### Why the model.p file is not the repository? + +This is the reason why we stated "should contain", at the beginning. +Unfortunately, due to its size (~350MB), it cannot be loaded on Github. To this end, we are storing it on one of [our servers](https://cso.kmi.open.ac.uk/download/model.p). To use the CSO Classifier v2, it is important that you download this model first. +However, for users' convenience this file will be automatically downloaded at the first run of the classifier. 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