The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present 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. 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 given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company.

Osborne, F., Salatino, A., Birukou, A., & Motta, E. (2016). Automatic classification of springer nature proceedings with smart topic miner. In The Semantic Web – ISWC 2016. ISWC 2016 (pp.383-399). Springer Verlag [10.1007/978-3-319-46547-0_33].

Automatic classification of springer nature proceedings with smart topic miner

Osborne F
;
2016

Abstract

The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present 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. 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 given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company.
Si
paper
Scientifica
Bibliographic data; Conference proceedings; Data mining; Metadata; Ontology learning; Scholarly data; Scholarly ontologies;
English
15th International Semantic Web Conference (ISWC) - OCT 17-21, 2016
978-3-319-46546-3
https://link.springer.com/chapter/10.1007/978-3-319-46547-0_33
Osborne, F., Salatino, A., Birukou, A., & Motta, E. (2016). Automatic classification of springer nature proceedings with smart topic miner. In The Semantic Web – ISWC 2016. ISWC 2016 (pp.383-399). Springer Verlag [10.1007/978-3-319-46547-0_33].
Osborne, F; Salatino, A; Birukou, A; Motta, E
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/381541
Citazioni
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 16
Social impact