The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
This addresses the need for better retrievability and analytics of scholarly articles in computer science, but it is incremental as it builds on existing ontology-driven methods.
The paper tackled the problem of automatically classifying research papers by topic using the Computer Science Ontology, resulting in a significant improvement over alternative methods as evaluated on a manually annotated gold standard.
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.