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Dynamically constructing semantic topic hierarchy through formal concept analysis

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Abstract

Dynamic topic analysis can examine the data from different perspectives and know the distribution of data with different correlation degrees thoroughly. It is a challenge to perform dynamic topic analysis on domain text data due to the smaller semantic differences among subtopics. This paper proposes a method of dynamically constructing topic hierarchy, which uses formal concept analysis (FCA)-based information retrieval (IR) as the technical basis and sememes as the semantic basis to perform hierarchical processing from fine-grained to coarse-grained on Chinese domain text data according to the topics of user’s query. It can meet the user’s need for different scales of the query results, and realize multi-angle inspection of the whole dataset and high-precision retrieval of the query. Taking sememes as formal attributes reduces the size of the concept lattice and expands the application of FCA technology to large-scale text data. The sememe-based word meaning identification (WMI) algorithm and semantic similarity measurement method for long text enable the topic hierarchy to be fine, and the coarse and fine filtering strategy renders the FCA-based method more efficient. Experimental results based on the open dataset show that the method proposed is an efficient and flexible topic-based hierarchical approach.

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Notes

  1. https://pypi.org/project/jieba/

  2. https://sourceforge.net/projects/inclose/

  3. https://radimrehurek.com/gensim/

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Availability of data and material

The data sets supporting the paper’s results and the tools used in the experiments are included within the paper and its additional files.

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Funding

This work is supported by the National Natural Science Foundation of China (61772152), the Basic Research Project (JCKY2019604C004), in part by the Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education of China (20YJCZH172).

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Fugang Wang & Nianbin Wang

  2. School of Physics and Electronic Engineering, Mudanjiang Normal University, Mudanjiang, 157011, China

    Fugang Wang & Wulin Zhang

  3. School of Electronic Information Engineering, Foshan University, Foshan, 528225, China

    Shaobin Cai

Authors
  1. Fugang Wang
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  2. Nianbin Wang
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  3. Shaobin Cai
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  4. Wulin Zhang
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Contributions

All authors conceived the idea of the study; Fugang W. and Wulin Z. collected the data and designed the experiments; Nianbin W. and Fugang W. analyzed the data; Shaobin C. and Fugang W. interpreted the results; Fugang W. wrote the paper; all authors discussed the results and revised the manuscript.

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Correspondence to Nianbin Wang.

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Wang, F., Wang, N., Cai, S. et al. Dynamically constructing semantic topic hierarchy through formal concept analysis. Multimed Tools Appl 82, 7267–7292 (2023). https://doi.org/10.1007/s11042-022-13640-2

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