한국건설관리학회논문집 (Korean Journal of Construction Engineering and Management)

  • 제21권6호
  • /
  • Pages.125-134
  • /
  • 2020
  • /
  • 2005-6095(pISSN)
  • /
  • 2465-9703(eISSN)
한국건설관리학회 (Korea Institute of Construction Engineering and Management)
권호 표지
DOI QR코드

DOI QR Code

문자열 유사도 알고리즘을 이용한 공종명 인식의 자연어처리 연구 - 공종명 문자열 유사도 알고리즘의 비교 -

Comparing String Similarity Algorithms for Recognizing Task Names Found in Construction Documents

  • 투고 : 2020.09.09
  • 심사 : 2020.10.19
  • 발행 : 2020.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

시공 서류에서 접하는 자연어는 당국에서 권장하는 언어와 크게 다르다. 일관성이 부족한 이러한 관행은 자동화를 통한 통합 연구를 방해하고 장기적으로 업계의 생산성을 저하시킬 것이다. 이 연구는 여러 문자열 유사성(문자열 일치) 알고리즘을 비교하여 여러 다른 방법으로 작성된 동일한 작업 이름을 인식하는 각 알고리즘의 성능을 비교하는 것을 목표로 한다. 우리는 또한 앞서 언급 한 편차가 얼마나 널리 퍼져 있는지에 대한 토론을 시작하는 것을 목표로 한다. 마지막으로, 우리는 실제로 발견된 시공 작업 이름을 형식에 비해 덜 복잡한 해당 작업 이름과 연결하는 작은 데이터 세트를 구성했다. 이 데이터 세트를 사용하여 미래의 자연어 처리 접근방식을 검증 할 수 있을 것으로 기대한다.

Natural language encountered in construction documents largely deviates from those that are recommended by the authorities. Such practice that is lacking in coherence will discourage integrated research with automation, and it will hurt the productivity in the industry for the long run. This research aims to compare multiple string similarity (string matching) algorithms to compare each algorithm's performance in recognizing the same task name written in multiple different ways. We also aim to start a debate on how prevalent the aforementioned deviation is. Finally, we composed a small dataset that associates construction task names found in practice with the corresponding task names that are less cluttered w.r.t their formatting. We expect that this dataset can be used to validate future natural language processing approaches.

키워드

참고문헌

  1. Abiteboul, S. (1997). Querying semi-structured data. InInternational Conference on Database Theory, Springer, Berlin, Heidelberg, pp. 1-18.
  2. An, M.G., Kim, M.J., and Kim, Y.S. (2019). "A BlockchainBased Risk Management for Overseas Construction Project Using Natural Language Processing." Proceedings of KICEM University Students Conference, pp. 116-119.
  3. Bilal, M., Oyedele, L.O., Qadir, J., Munir, K., Ajayi, S.O., Akinade, O.O., and Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities and future trends. Advanced engineering informatics, 30(3), pp. 500-521. https://doi.org/10.1016/j.aei.2016.07.001
  4. Cambria, E., and White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), pp. 48-57. https://doi.org/10.1109/MCI.2014.2307227
  5. Han, K.K., and Golparvar-Fard, M. (2017). Poten-tial of big visual data and building informationmodeling for construction performance analyt-ics: An exploratory study. Automation in Con-struction, 73, pp. 184-198. https://doi.org/10.1016/j.autcon.2016.11.004
  6. Kim, Y.R., Lee, S.H., and Park, S.H. (2012). "Development of Rule-Set Definition for Architectural Design Code Checking based on BIM." Korean Journal of Construction Engineering and Management, KICEM, 13(6) pp. 143-152. https://doi.org/10.6106/KJCEM.2012.13.6.143
  7. Lee, J.H. (2019). "Review on Natural Language Processing Reasearch Utilizing Unstructured Text Data in Construction Industry." Construction Engineering and Management, 20(2), pp. 62-66.
  8. Lee, S.K., Kim, K.R., and Yu, J.H. (2012). Automatic Inference of Standard BOQ (Bill of Quantities) Items using BIM and Ontology. Korean Journal of Construction Engineering and Management, 13. 10.6106/KJCEM.2012.13.3.099.
  9. Lalwani, M., Bagmar, N., and Parikh, S. (2014). "Efficient Algorithm for Auto Correction Using ngram Indexing." International Journal of Computer & Communication Technology (IJCCT), 3(3), pp. 23-27.
  10. McGuinness, D. L. (2004). OWL web ontology language overview. W3C recommendation. http://www.w3.org/TR/owl-features.
  11. Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3-26. https://doi.org/10.1075/li.30.1.03nad
  12. Park, Y.S., Oh, C.D., Jeon, Y.S., and Park, C.S. (2008). "A Web-Based Construction Failure Information System using Case-Based Reasoning." Korean Journal of Construction Engineering and Management, KICEM, 9(6), pp. 257-267.
  13. Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), pp. 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  14. Williams, T.P., and Gong, J. (2014). "Predicting construction cost overrunsusing text mining, numerical data and ensemble classifiers." Automation in Construction, 43, pp. 23-29. https://doi.org/10.1016/j.autcon.2014.02.014
  15. Zhang, J., and El-Gohary, N.M. (2016). "Semantic nlp-based information extrac-tion from construction regulatory documents for automated compliancechecking." Journal of Computing in Civil Engineering, 30(2), 04015014. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000346
  16. Y.S. Kim. (2019). Automatic multi-label image classification model for construction site images (Unpublished doctoral dissertation). Graduate School, Seoul National University.
  17. Eunjeong L. Park, Sungzoon Cho. (2014). KoNLPy: Korean natural language processing in Python. 26th Annual Conference on Human and Language Technology, pp. 1-4.
  18. KAIST CILab (2014). Hannanum Morphological Analysis http://semanticweb.kaist.ac.kr/hannanum/index.html