Smart Topic Miner (STM) is an ontology-driven application developed to assist Springer Nature’s editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. By leveraging the Computer Science Ontology—a comprehensive taxonomy of research areas—STM semantically analyzes and matches editorial products with relevant topics. This tool aims to streamline the traditionally manual and time-consuming process of topic annotation, enhancing the discoverability of publications in online libraries.
Benefits & outcomes
- Significant reduction in the time required to annotate proceedings.
- Improved discoverability of publications, resulting in 9.3 million additional downloads.
- Enhanced metadata quality through consistent and accurate topic annotations.
- Positive usability feedback from editorial staff across multiple countries.
Notes & quotes
“Our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads.”
“We also present a user study involving 9 editors, which yielded excellent results in terms of usability.”
STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, processing about 800 volumes per year.
Publisher / company: Springer Nature
Date of case study: 24/03/2021
Source: https://arxiv.org/abs/2103.13527