## 2 Research Background  
### Knowledge Management in Construction  
KM is an integrated, systematic approach to identify, manage, and share an organization's knowledge [17]. It enables individuals to collaboratively create new knowledge and ultimately aiding in achieving the organization's objectives [18]. Effective KM is also critical in large-scale and complex construction projects. Globally, many engineering corporations worldwide are facing difficulties due to losing experienced professionals and the indispensable knowledge and skills they bring [19]. As many engineers approach retirement age, there is an evident lack of qualified junior engineers to take their places. Thus, it is necessary to transfer the tacit knowledge of seasoned professionals to organizational assets to replace senior engineers, managers, and experienced workers effectively. From a broader perspective, managing knowledge effectively ensures that the organization is utilizing all the resources available in the most effective manner to improve organizational performance [6]. However, many organizations face a lack of well-documented and integrated KM processes within their systems, especially in capturing and learning from past experiences [20]. To address these challenges, an effective KM system should incorporate methodologies that ensure continuous learning and knowledge sharing across the organization. A systematic approach to managing and sharing organizational knowledge helps in achieving organizational objectives efficiently and effectively, reducing the risk of knowledge loss in the complex environment [21]. In this context, the incorporation of innovative technologies becomes pivotal. KM in the construction domain has been of interest to many researchers and practitioners. Previous KM research dominantly dealt with IT-based KM approach was performed from diverse perspectives, such as assessing the impact of KM [22], developing a KM platform [2; 18; 23], investigating KM strategy [4; 9; 10; 24; 25; 26]. In the meantime, with the development of NLP technology, researchers have been interested in how to apply text analytics techniques from the perspective of document management and knowledge extraction. [PERSON] (2006) proposed an ontology-based KM system that provides information retrieval service [18]. Based on the construction ontology expanded through industrial foundation classes (IFC). The proposed KM tool initially summarizes documents as index terms based on frequency. Then, the extracted index terms are matched against the construction ontology to retrieve relevant information with the user query. [PERSON] and [PERSON] (2016) presented a machine learning model that classifies text, whether the given clause of a general contract belongs to a specific category or not [11]. In this study, they applied the bag of words for feature selection and the term frequency-inverse document frequency (TF-IDF) for feature weighting. [PERSON] et al. introduced a named entity recognition (NER) model for automatic review of construction specifications [14]. They trained a neural network model to extract a word (or phrase) from specification documents and classify it into predefined categories: organization, action, element, standard, and reference. In the following study, [PERSON] et al. proposed a web-based KM system that determines whether the given two provisions are relevant or not [1]. In this study, they used a semantic construction thesaurus developed by an ML-based word embedding technique. Despite the application of existing KM tools and techniques has produced meaningful fruits, previous approaches have limitations. First, previous IT-based KM tools focused on information retrieval rather than information extraction. Keyword-based information retrieval searches for related documents based on the presence or absence of specific terms without considering the entire context of the text. Some previous KM tools applied ontology and NLP techniques to take into account the semantics of the text.