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Early Detection and Forecasting of Research Trends

Salatino, Angelo (2015). Early Detection and Forecasting of Research Trends. In: 14th International Semantic Web Conference, 11-15 October 2015, Bethlehem (PA), USA.

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Abstract

Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others. Currently, this task is performed either by domain experts, with the assistance of tools for exploring research data, or by automatic approaches. The constant increase of research data makes the second solution more appropriate, howeverautomatic methods suffer from a number of limitations. For instance, they are unable to detect emerging but yet unlabelled research areas (e.g., Semantic Web before 2000). Furthermore, they usually quantify the popularity of a topic simply in terms of the number of related publications or authors for each year; hence they can provide good forecasts only on trends which have existed for at least 3-4 years. This doctoral work aims at solving these limitations by providing a novel approach for the early detection and forecasting of research trends that will take advantage of the rich variety of semantic relationships between research entities (e.g., authors, workshops, communities) and of social media data (e.g., tweets, blogs).

Item Type: Conference or Workshop Item
Copyright Holders: 2015 The Author
Keywords: scholarly data; research trends; trend detection; trend forecasting; semantic web technologies
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 44643
Depositing User: Angelo Salatino
Date Deposited: 16 Oct 2015 08:29
Last Modified: 04 Oct 2016 19:45
URI: http://oro.open.ac.uk/id/eprint/44643
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