This paper aims to provide an in-depth examination of the rapidly advancing field of multi-modal knowledge graphs for engineering management (KGEM). It explores the current research landscape, highlighting significant technological innovations and identifying promising areas for future exploration and development. The aim is to offer a comprehensive review, focusing on how multi-modal knowledge graphs can enhance engineering management by integrating diverse data sources, improving decision-making processes and optimizing resource allocation.
To conduct a comprehensive review, this study adopts a hybrid review methodology, combining systematic review techniques with a narrative synthesis approach. It systematically examines the body of research on multi-modal KGEM, extracting and synthesizing key findings across diverse studies. The review process adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring a transparent and rigorous selection of relevant literature. Additionally, the study identifies significant gaps in the existing research landscape and outlines potential avenues for future exploration, with a focus on technological advancements in data integration and decision support systems.
This study provides a comprehensive examination of the current state of multi-modal KGEM, identifying three key themes: (1) data integration and intelligent information management, (2) risk management and resource optimization and (3) decision support and intelligent assistance. From these themes, three major gaps and requirements are summarized: (1) the need for more sophisticated methods to integrate and manage diverse data sources, (2) the development of advanced techniques for better risk assessment and resource allocation and (3) the enhancement of decision support systems through intelligent assistance and real-time data analysis. These findings highlight the critical areas requiring further technological innovation and research.
This paper provides an in-depth analysis of multi-modal KGEM, synthesizing existing research and emphasizing the critical role of integrating diverse data sources and advanced decision support systems. By adopting a hybrid review methodology and adhering to PRISMA guidelines, the study offers novel insights into the integration of multi-modal data, risk management and intelligent assistance. Key contributions include identifying significant technological advancements, synthesizing current research trends and proposing new avenues for future exploration. These innovations aim to enhance the planning, design, construction and operational phases of engineering projects, driving technological progress and improving overall project efficiency and effectiveness.
