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Discovering and Visualizing Interdisciplinary Content Classes in Scientific Publications

Text visualization is a rather important task related to scientific corpora, since it provides a way of representing these corpora in terms of content, leading to reinforcement of human cognition compared to abstract and unstructured text. In this paper, we focus on visualizing funding-specific scientific corpora in a supervised context and discovering interclass similarities which indicate the existence of inter-disciplinary research. This is achieved through training a supervised classification — visualization model based on the arXiv classification system. In addition, a funding mining submodule is used which identifies documents of particular funding schemes. This is conducted, in order to generate corpora of scientific publications that share a common funding scheme (e.g. FP7-ICT). These categorized sets of documents are fed as input to the visualization model in order to generate content representations and to discover highly correlated content classes. This procedure can provide a high level monitoring which is important for research funders and governments in order to be able to quickly respond to new developments and trends.

Theodoros Giannakopoulos, Yannis Foufoulas, Eleftherios Stamatogiannakis, Harry Dimitropoulos, Natalia Manola, Yannis Ioannidis, "Discovering and Visualizing Interdisciplinary Content Classes in Scientific Publications ", D-Lib Magazine, 20(11), 4, 2014
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D-Lib Magazine, 20(11) , 4
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