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Integrating XBRL data with textual information in Chinese: A semantic web approach

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  • Chou, Chi-Chun
  • Chang, C. Janie
  • Peng, Jacob

Abstract

Due to formatting differences, the difficulties of processing the textual disclosures and integrating them with quantitative financial data are well documented in the literature. Using a design science methodology, this paper describes a method that automatically extracts relevant textual data from annual reports published in Chinese. These extracted words are then mapped to a knowledge framework we proposed. This paper shows that it is technologically feasible to reorganize the MD&A contents into any given knowledge structure to improve the search capability, readability, and cohesiveness of the MD&A contents. Finally, we demonstrate a prototype system that uses semantic web technology to achieve information integration that presents XBRL formatted accounting data with relevant textual disclosures together to assist user decision making.

Suggested Citation

  • Chou, Chi-Chun & Chang, C. Janie & Peng, Jacob, 2016. "Integrating XBRL data with textual information in Chinese: A semantic web approach," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 32-46.
    • Handle: RePEc:eee:ijoais:v:21:y:2016:i:c:p:32-46
    DOI: 10.1016/j.accinf.2016.04.002
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    References listed on IDEAS

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    Cited by:

    1. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    2. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Tan, Kim Hua & Gupta, Shivam, 2021. "Validating the impact of accounting disclosures on stock market: A deep neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    3. Chou, Chi-Chun & Hwang, Nen-Chen Richard & Wang, Tawei & Debreceny, Roger, 2018. "The topical link model-integrating topic-centric information in XBRL-formatted reports," International Journal of Accounting Information Systems, Elsevier, vol. 29(C), pages 16-36.
    4. Sharifah Milda Amirul & Noor Ismawati Jaafar & Anna Azriati Che Azmi, 2022. "Two decades of XBRL: a science mapping of research trends and future research agenda," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2301-2324, August.
    5. Zhuoqian Liang & Ding Pan & Yuan Deng, 2020. "Research on the Knowledge Association Reasoning of Financial Reports Based on a Graph Network," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    6. Senave, Elseline & Jans, Mieke J. & Srivastava, Rajendra P., 2023. "The application of text mining in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).

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