Empirical analysis on a keyword-based semantic system

Abstract

Keywords in scientific articles have found their significance in information filtering and classification. In this article, we empirically investigated statistical characteristics and evolutionary properties of keywords in a very famous journal, namely Proceedings of the National Academy of Science of the United States of America (PNAS), including frequency distribution, temporal scaling behavior, and decay factor. The empirical results indicate that the keyword frequency in PNAS approximately follows a Zipf's law with exponent 0.86. In addition, there is a power-low correlation between the cumulative number of distinct keywords and the cumulative number of keyword occurrences. Extensive empirical analysis on some other journals' data is also presented, with decaying trends of most popular keywords being monitored. Interestingly, top journals from various subjects share very similar decaying tendency, while the journals of low impact factors exhibit completely different behavior. Those empirical characters may shed some light on the in-depth understanding of semantic evolutionary behaviors. In addition, the analysis of keyword-based system is helpful for the design of corresponding recommender systems.

9 pages, 1 table and 4 figures

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• Computational Socioeconomics
• Hypergraph model of social tagging networks
• Collaborative filtering with diffusion-based similarity on tripartite graphs
• Evolution of scaling emergence in large-scale spatial epidemic spreading
• Scaling Laws in Human Language
• Empirical analysis on a keyword-based semantic system
• The Aging Effect in Evolving Scientific Citation Networks
• Individual popularity and activity in online social systems
• Self-organization in social tagging systems
• Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset
• Scaling in the global spreading patterns of pandemic Influenza A (H1N1) and the role of control: empirical statistics and modeling
• Effective Personalized Recommendation in Collaborative Tagging Systems