AUGUR: Forecasting the Emergence of New Research Topics

“AUGUR: Forecasting the Emergence of New Research Topics” is a paper submitted to the ACM/IEEE Joint Conference on Digital Libraries 2018, presented on June 5 2018, in Fort Worth, TX, USA   Angelo Salatino, Francesco Osborne and Enrico Motta   Abstract Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, … Read more

Department Research Seminar: Early Detection of Research Topics

On the 8th February I delivered a seminar to my department (KMi @ OU) in which I described the work I have been doing in the last two years for my postgraduate research.

I started with a little bit of introduction about science. Shortly, I moved to the currently available technologies for keeping track of the development of the different research areas. I showed how this technologies were not satisfactory enough if we want to perform an early detection of research topics.

In presenting, the state of the art (including The Structure of Scientific Revolution by Kuhn), I could state my main hypothesis, regarding the existence of an embryonic stage that research areas face, and that it is possible to detect their emergence during this stage1.

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Smart Topic Miner

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 an editor and produces a structured set of research topics and a number of Springer Nature Classification tags, which best characterise the proceedings book. Indeed, if you regularly publish in the main Computer Science conferences, your work was probably already classified and indexed by using STM. **

During the classification of proceedings, editors are involved in different tasks and one of them is determining the list of related terms and categories. This is accomplished according to their own experience, like exploring titles and abstracts visually. However, this appears to be time-consuming as well as complex to perform. In addition, new emerging topics may not find their space while some other current topics could be considered still popular.

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