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Semantic Knowledge Representation of Administrative Change Processes in Construction Projects with Artificial Intelligence

Title:

Semantic Knowledge Representation of Administrative Change Processes in Construction Projects with Artificial Intelligence

Sanatgar Baboldashti, Amirhossein (2025) Semantic Knowledge Representation of Administrative Change Processes in Construction Projects with Artificial Intelligence. Masters thesis, Concordia University.

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Abstract

Knowledge is considered a valuable asset, and knowledge management is becoming increasingly popular in the construction industry. On the one hand, the knowledge produced during construction projects is project-oriented, experiential, and context-specific. On the other hand, the use of various project actors, each employing their own terminologies and mental lexicons to express concepts, has resulted in semantic ambiguity issues within project management systems. In certain specific areas, the severity of this ambiguity amplifies, particularly when compounded by inherent confusion within domain-specific terminologies. For instance, due to the heterogeneity and lexical ambiguities present in change-related terminology, the process of deducing the true semantics associated with the terms is typically conducted by humans. Furthermore, data-dependent analyses and the demanding preprocessing required in change analysis studies, coupled with the resistance among project stakeholders to openly share change-related data—especially in cases involving claims and disputes—constrain access to a substantial volume of reliable data on changes in past projects. Hence, this research addressed these gaps by (1) establishing an unambiguous knowledge model for advancing administrative change process management constitutes the symbolic AI contribution of this work through its use of formal ontology modeling; (2) facilitating information retrieval and offering a unified structure for change-related data; and (3) proposing a hybrid method that integrates data-driven techniques, symbolic AI, and knowledge-based approaches in constructing the ontology.The proposed methods employ a dual approach, combining bottom-up data-driven techniques with a top-down knowledge-based approach to develop the change management ontology (CMOnto). Analyzing project data, along with leveraging tacit knowledge from industry experts, provides a rich and comprehensive resource for model development. In the development process of CMOnto, 50,000 change-relevant data points from various project types were used for text mining to identify key concepts by segmenting the data into distinct phrases. After extracting the main concepts, they were integrated with relevant concepts from a thorough analysis of domain ontologies. The concepts were further validated and refined with insights from industry experts, and a taxonomy was constructed to visually represent the hierarchical classification of domain entities. Subsequently, all concepts were modeled in an ontology tool editor, and the results underwent evaluation. The evaluation encompassed consistency checking, competency questions, assessments from five domain experts, and semi-structured interviews involving 18 experts.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Sanatgar Baboldashti, Amirhossein
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Civil Engineering
Date:3 November 2025
Thesis Supervisor(s):Nik-Bakht, Mazdak
Keywords:Change Management, Ontology, Knowledge Management
ID Code:996745
Deposited By: AmirHossein Sanatgar Baboldashti
Deposited On:29 Jun 2026 14:38
Last Modified:29 Jun 2026 14:38

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