The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

“The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles” is a full paper submitted to the TPDL 2019: 23rd International Conference on Theory and Practice of Digital Libraries, 9-12 September 2019 OsloMet – Oslo Metropolitan University, Oslo, Norway

This paper was also nominated for the Best Paper Award, see here: http://www.tpdl.eu/tpdl2019/awards/

Authors

Angelo A. Salatino, Francesco Osborne, Thiviyan Thanapalasingam, Enrico Motta

Abstract

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 paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), 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.

CSO Classifier

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.

Figure1: Framework of CSO Classifier

Paper Download

Download paper (from ORO): link

Download paper (from DOI): link

 

Slides of the talk


 

Media

 

Open Resources

Gold Standard

We built our gold standard by asking 21 domain experts to classify 70 papers in terms of topics drawn from the CSO ontology.
We queried the MAG dataset and selected the 70 most cited papers published in 2007-2017 within the fields of “Semantic Web”, “Natural Language Processing”, and “Data Mining”. We then contacted 21 researchers in these fields at various levels of seniority and asked each of them to annotate 10 of these papers. We structured the data collection in order to have each paper annotated by at least three experts, using the majority vote to address disagreements. The papers were randomly assigned to experts, while minimising the number of shared papers between each pair of experts.
The Gold Standard is JSON file containing a dictionary of 70 items (papers). Each item has a 32 alphanumerical characters key representing the id of the paper and its value is also a dictionary structured as shown in the following tables.

 

paper object

Key Type Info
“doi” string DOI of the paper
“title” string title of the paper
“abstract” string abstract of the paper
“keywords” list author keywords
“doc_type” string type of document, it identifies whether it is a conference paper, or journal, or others
“topics” list Fields of Science identified by Microsoft Academic Graph. This information is not used during the process of classification.
“source” string Source topic, whether it comes from the field of “Semantic Web”, “Natural Language Processing”, or “Data Mining”
“citations” numerical Number of citation at the time of download
“gold_standard” dictionary an object containing the information obtained by the experts and the generated gold standard ( specifications)
“cso_output” dictionary an object containing the output of the CSO Classifier ( specifications)

gold_standard object

Key Type Info
“relevant_rater_A” list relevant topics selected by the first expert during the annotation process
“relevant_rater_B” list relevant topics selected by the second expert during the annotation process
“relevant_rater_C” list relevant topics selected by the third expert during the annotation process
“majority_vote” list set of topics selected using the majority vote approach over the relevant topics chosen by the experts
“enhancement_majority_vote” list set of enhanced topics of the majority vote set

cso_output object

Key Type Info
“syntactic” list list of topics returned by the syntactic module
“semantic” list list of topics returned by the semantic module
“enhancement” list list of enhanced topics from the union of the result of semantic and syntactic module
“final” list the final set of topics from the CSO Classifier

Download Gold Standard (74KB): link

 

Code Python

The Python implementation of the CSO Classifier is available through our Github repository: Go to Github repo

Or you can simply install the package by running:

pip install cso-classifier

 

Word2Vec Model

We applied the word2vec approach to a collection of text from the Microsoft Academic Graph (MAG) for generating word embeddings. We first downloaded titles, and abstracts of 4,654,062 English papers in the field of Computer Science. Then we pre-processed the data by replacing spaces with underscores in all n-grams matching the CSO topic labels (e.g., “digital libraries” became “digital_libraries”) and for frequent bigrams and trigrams (e.g., “highest_accuracies”, “highly_cited_journals”). Finally, we trained the word2vec model using the parameters provided in the following table. The parameters were set to these values after testing several combinations.

Parameter Value
method skipgram
embedding size 128
window size 10
negative 5
max iteration 5
min-count cutoff 10

Download Word2vec model (299MB): link

 

Pseudocodes

Here are the pseudocodes of both syntactic and semantic modules:

Figure 2. Pseudocode of the syntactic module

 

Figure 3. Pseudocode of the semantic module

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