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

TL;DR

The paper introduces the CSO Classifier, an unsupervised method that automatically classifies research papers into computer science topics using ontology-based concepts, enhancing retrieval and analysis.

Contribution

It presents a novel ontology-driven unsupervised classifier for research topic detection in scholarly articles, leveraging the Computer Science Ontology.

Findings

Significant improvement over alternative classification methods

Effective in extracting relevant research concepts from metadata

Validated on a gold standard dataset

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 re-search 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.