A new solution for classifying scholarly publications: Smart Topic Miner

The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. This process is typically carried out manually by expert editors, leading to high costs and slow throughput. For these reasons, the Rexplore team, in collaboration with Springer Nature, created Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas.

STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family, consisting in about 800 proceedings books each year. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature Classification tags, which best characterise the proceedings book. Differently from other applications which characterize a text with topics, STM produces a full taxonomy of the relevant research areas rather than a flat list of keywords or categories. This helps editors and users to understand the context of each topic and its relationships with other research areas.

You can try a public demo of STM at http://rexplore.kmi.open.ac.uk/STM_demo/

Relevant paper:

• Osborne, F., Salatino, A., Birukou, A. and Motta, E. (2016) Automatic Classification of Springer Nature Proceedings with Smart Topic Miner. International Semantic Web Conference 2016, Kobe, Japan. – slides