Refining the Measurement of Topic Similarities through Bibliographic Coupling and LDA

Omer Hanif*, Zhu Donghua, Wang Xuefeng, M. Saqib Nawaz

*此作品的通讯作者

    科研成果: 期刊稿件文章同行评审

    5 引用 (Scopus)

    摘要

    Generally, two topics with vastly different terminology probably indicate different implied concepts. However, these topics themselves might share common references (bibliographic coupling), which suggest the underlying joint concept. Therefore, searching for these joint concepts in different topics would be of scientific interest. Previous studies have measured the similarity between topics based on comparison of the topics' word probability distributions. In contrast, this paper presents an approach for measuring the similarity between topics based on the bibliographic coupling. Besides, the similarity is independent of the topic's word probability distributions generated by a Latent Dirichlet Allocation (LDA) model. The proposed approach was evaluated using its counterpart (intra-topic similarity), baseline topic similarity matrices, and cosine measure. The method was exampled on brain cancer patents. A cross-topic similarity network of eight topics showcases 28 cross-topic pairs to profile which topics were associated with particular topics. Interestingly, some of the 28 combinations may be of scientific interest. For instance, the findings of the top five cross-topic pairs suggest that 'growth of cancer cells' and 'imbalances in the hormones' have common knowledge sources with the highest similarity value. These two entirely different concepts may suggest some common causative factors within the field. We believe that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.

    源语言英语
    文章编号8928557
    页(从-至)179997-180011
    页数15
    期刊IEEE Access
    7
    DOI
    出版状态已出版 - 2019

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