“Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies” is poster paper presented at the Poster and Demo session [D45] on Wednesday 19th October 2016 at the 15th International Semantic Web Conference in Kobe, Japan
Authors:
Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou and Enrico Motta
Abstract:
Academic publishers, such as Springer Nature, annotate scholarly products with the appropriate research topics and keywords to facilitate the marketing process and to support (digital) libraries and academic search engines. This critical process is usually handled manually by experienced editors, leading to high costs and slow throughput. In this demo paper, we present Smart Topic Miner (STM), a semantic application designed to support the Springer Nature Computer Science editorial team in classifying scholarly publications. STM analyses conference proceedings and annotates them with a set of topics drawn from a large automatically generated ontology of research areas and a set of tags from Springer Nature Classification.
Couple of months ago, with my team, we attended the Springer Nature HackDay (here is the post). Just not long ago, Springer Nature released a short video featuring us. Summarised is also my interview, in which I discuss the advantages of making scholarly datasets, as SciGraph, available to anyone. https://youtu.be/0D8NAlSQ_WY Other media Building on the success of the first Springer Nature #SciGraph Hack Day, we…
Smart Topic Miner (STM) is a web application which uses Semantic Web technologies to classify scholarly publications on the basis of Computer Science Ontology (CSO), 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…
The Smart Book Recommender (SBR) is a semantic application designed to support the Springer Nature editorial team in promoting their publications at Computer Science venues. It takes as input the proceedings of a conference and suggests books, journals, and other conference proceedings that are likely to be relevant to the attendees of the conference in question. It does so by taking advantage of a semantic representation of…
