Open AccessThe application of weighted co‐occurring keywords time gram in academic research temporal sequence discovery
Author(s) -
Li Shuqing,
Sun Ying
Publication year - 2013
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.14505001037
Subject(s) - computer science , visualization , sequence (biology) , time sequence , n gram , data mining , information retrieval , temporal database , data science , artificial intelligence , language model , biology , genetics
The discovery and visualization of temporal sequence of personalized academic research can enhance the ability for discovering the latent trend of interests to information seekers. In this paper, we propose a definition of weighted co‐occurring keywords time gram and use it as a basic unit to analyze the temporal information in existed keywords collection. We further propose a method to get the temporal sequence and temporal network based on these time grams. An application of the proposed method in discovering academic research temporal sequence is discussed, which includes techniques for acquiring extended keywords, assigning weight to each keyword and co‐occurring weight to each keyword pair. A visualization tool is designed for browsing the temporal networks identified. Finally, we report an experiment in the area of library and information studies. The experiment results show the effectiveness of the proposed method in helping users analyzing and portraying the evolution pattern and developing trend of corresponding academic research.