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Title: Semantic Similarity versus Co-authorship Networks: A Detailed Comparison
Authors: Paraschiv, Ionut Cristian
Dascalu, Mihai
Trausan-Matu, Stefan
Nistor, Nicolae
Montes de Oca, Ambar Murillo
McNamara, Danielle S.
Keywords: semantic similarity
discourse analysis
co-authorship networks
social network analysis
Issue Date: 2017
Publisher: IEEE
Citation: Paraschiv, I. C., Dascalu, M., Trausan-Matu, S., Nistor, N., Montes de Oca, A. M., & McNamara, D. S. (2017). Semantic Similarity versus Co-authorship Networks: A Detailed Comparison. In 3rd Int. Workshop on Design and Spontaneity in Computer-Supported Collaborative Learning (DS-CSCL-2017), in conjunction with the 21th Int. Conf. on Control Systems and Computer Science (CSCS21) (pp. 566–570). Bucharest, Romania: IEEE.
Abstract: Whether interested in personal work, in learning about trending topics, or in finding the structure of a specific domain, individuals' work of staying up-to-date has become more and more difficult due to the increasing information overflow. ln our previous work our focus has been to create a semantic annotation model accompanied by dedicated views to explore the semantic similarities between scientific articles. This paper focuses on applying our approach on a dataset of 519 project proposal abstracts, with the intention to bring value to the current indexation methodologies that rely primarily on co- citations and keyword matching. Our experiment uses various Social Network Analysis metrics to compare the rankings generated by two complementary models relying on semantic similarity and co-authorship networks. The two models are statistically different based on representative project associations, are significantly correlated in terms of project rankings by eccentricity and closeness centrality, and the semantic similarity network is denser.
Appears in Collections:1. RAGE Publications

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