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2025 Vol.4, Issue 1

Research Article

Automated Development of Domain-specific Thesaurus for Text Mining in the Construction Industry

건설산업 텍스트마이닝을 위한 도메인 특화 시소러스 자동 구축

Youmin Park1
,
Sangyeol Yu2
,
Jinwoo Kim3
,
Seonghyeon Moon4*
박 유민1
,
유 상열2
,
김 진우3
,
문 성현4*
1Master‘s Student, School of Integrated Technology, Gwangju Institute of Science and Technology
2Master's Student, School of Industrial Systems Engineering, Gyeongsang National University
3Life Member, Assistant Professor, Department of Civil and Environmental Engineering, Hanyang University
4Corresponding Author, Life Member, Assistant Professor, School of Industrial Systems Engineering, Gyeongsang National University
1광주과학기술원 융합기술학제학부 석사과정
2경상국립대학교 산업시스템공학부 석사과정
3종신회원 ‧ 한양대학교 건설환경공학과 조교수
4교신저자 ‧ 종신회원 ‧ 경상국립대학교 산업시스템공학부 조교수
*Corresponding Author
31 March 2025. pp. 1-8
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Abstract

Text mining techniques are actively utilized in the construction industry to automatically analyze documents and extract experiential knowledge from the field. However, challenges persist in leveraging Natural Language Processing (NLP) and Large Language Models (LLMs) due to the specialized terminology and unique expressions prevalent in the construction domain. This study proposes a methodology for the automated construction of a domain-specific thesaurus tailored to text mining applications in the construction industry. Using construction specification data, we employed co-occurrence analysis, Word2Vec, and cosine similarity to construct a thesaurus comprising 748,890 co-occurring words and 172,650 word embeddings. Furthermore, word associations and their interrelationships were analyzed and visualized using a word network. The thesaurus developed in this study facilitates precise correction of word recognition errors by leveraging domain-specific characteristics, thereby significantly enhancing the accuracy of text analysis for construction-related documents.

Keywords
Text Mining
Natural Language Processing
Thesaurus
Construction Industry

건설산업에 존재하는 문서를 자동으로 분석하여 현장의 경험지식을 획득하기 위해 텍스트마이닝 기술이 활발하게 사용되고 있다. 하지만 건설산업의 전문용어와 특수한 표현으로 인해 자연어처리(Natural Language Processing; NLP) 및 대규모 언어 모델(Large Language Model; LLM) 기술을 활용하는 것에 어려움이 존재한다. 본 연구는 건설산업의 도메인 특성을 활용한 텍스트마이닝을 위해 건설산업 특화 시소러스를 자동으로 구축하는 방법론을 제안한다. 건설공사 시방서 데이터를 바탕으로 Co-occurrence, Word2Vec, Cosine Similarity를 활용하여 748,890개의 동시 등장 단어와 172,650개의 분산 표현 기반 시소러스를 구축하였으며, Word Network을 통해 단어 간 연관성 파악 및 시각화를 수행했다. 본 연구에서 구축한 시소러스를 활용하면 입력 데이터의 도메인 특성을 파악하여 문서 내 단어 인식 오류를 정확히 수정할 수 있으며, 건설산업 문서에서 텍스트 분석의 정확도를 향상시킬 수 있다.

키워드
텍스트마이닝
자연어처리
시소러스
건설산업
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Information
  • Publisher :Korean Society of Automation and Robotics in Construction
  • Publisher(Ko) :(사)한국건설자동화·로보틱스학회
  • Journal Title :Journal of Construction Automation and Robotics
  • Journal Title(Ko) :건설자동화·로보틱스 논문집
  • Volume : 4
  • No :1
  • Pages :1-8
  • Received Date : 2024-12-09
  • Revised Date : 2025-03-25
  • Accepted Date : 2025-03-26
  • DOI :https://doi.org/10.55785/JCAR.4.1.1
Journal Informaiton Journal of Construction Automation and Robotics Journal of Construction Automation and Robotics
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