當一般民眾與讀者想要快速瞭解一法規修正文件內容之具體修正內容與修正歷程時，其往往先透過網絡前往政府網站或其他權威網站尋得自己想要了解之法律法規。然而，因為政府網站與其他權威網站只會提供每次修正後之完整法規法條內容，不會標註修正之內容，民眾需再將蒐集到之法規修正前後內容進行逐字比對，彙整出法規修正前後之差別，以找到法規修正之具體內容與細節，以達到了解法規修正內容之目的。故在上述過程極易導致讀者出現耗費大量時間比對，但仍然出現遺漏、出錯、修正處並非民眾與讀者想要了解、修正處為非重要修正等一系列問題。
為解決上述問題，本研究乃先從政府網站與其他權威網站蒐集法規修正文件，並解析對應文件所包含之特徵屬性並歸納此些特徵屬性之表達方式。其次，本研究以先前解析之結果作為發展「法規重要修正內容整合」方法論，該方法論可從法規修正文件中擷取對應之特徵屬性；再次，該方法用K-平均分群方法將所有蒐集到之法規修正文件分為多個時間點之法規修正文件族群；而後，此方法乃利用餘弦相似性對兩兩法規修正文件內容之相速度進行計算與比較，並依此結果判定各法規修正文件內容族群中存在重要修正部分具有較多修正與刪減之法規修正文件內容；最後，此方法將各法規修正文件內容族群中具有較多修正與刪減之法規修正文件內容所包含之關鍵修正內容以視覺化之方式呈現，以幫助一般民眾與讀者快速掌握法規修正之具體內容，從而避免讀者因閱讀錯誤或遺漏法規修正處而導致違反法規的情事。

When the general public and readers seek to understand the specific amendments and history of a regulatory document, they typically start by searching for the desired laws and regulations on government or other authoritative websites via the internet. However, as these sites only provide the complete legal text after each amendment without indicating the specific changes, individuals must compare the pre- and post-amendment texts word by word to compile the differences. This comparison is necessary to identify the details of the amendments and to understand the regulatory changes. This process is prone to causing readers to spend a considerable amount of time comparing texts while still potentially encountering issues such as omissions, errors, amendments that are not of interest to the reader, or insignificant amendments.

1. Wagh, R. S. (2013). Knowledge discovery from legal documents dataset using text mining
techniques. International Journal of Computer Applications, 66(23).
2. Chen, Y. L., Liu, Y. H., & Ho, W. L. (2013). A text mining approach to assist the general
public in the retrieval of legal documents. Journal of the American Society for Information
Science and Technology, 64(2), 280-290.
3. Sagayam, R., Srinivasan, S., & Roshni, S. (2012). A survey of text mining: Retrieval,
extraction and indexing techniques. International Journal of Computational Engineering
Research, 2(5), 1443-1446.
4. Chan, B., Wu, L., Talbot, J., Cammarano, M., & Hanrahan, P. Vispedia: Interactive Visual
Exploration of Wikipedia Data via.
5. Godfrey, D., Johns, C., Meyer, C., Race, S., & Sadek, C. (2014). A case study in text
mining: Interpreting twitter data from world cup tweets. arXiv preprint arXiv:1408.5427.
6. Lachana, Z., Loutsaris, M. A., Alexopoulos, C., & Charalabidis, Y. (2020). Automated
analysis and interrelation of legal elements based on text mining. International Journal of
E-Services and Mobile Applications (IJESMA), 12(2), 79-96.
7. Zakharova, A. A., Podvesovskii, A. G., & Shklyar, A. V. (2019). Visual and cognitive
interpretation of heterogeneous data. The International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences, 42, 243-247.
8. Rossi, A., & Palmirani, M. (2018). From words to images through legal visualization. In AI
Approaches to the Complexity of Legal Systems: AICOL International Workshops 2015-
2017: AICOL-VI@ JURIX 2015, AICOL-VII@ EKAW 2016, AICOL-VIII@ JURIX 2016,
AICOL-IX@ ICAIL 2017, and AICOL-X@ JURIX 2017, Revised Selected Papers 6 (pp.
72-85). Springer International Publishing.
9. Don, A., Zheleva, E., Gregory, M., Tarkan, S., Auvil, L., Clement, T., ... & Plaisant, C.
(2007, November). Discovering interesting usage patterns in text collections: integrating
text mining with visualization. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (pp. 213-222).