Abstract—Current asset management approaches struggle to represent asset information involving multidimensional dependencies namely spatial, temporal, and semantic, where it is often stored in isolation rather than interconnected elements. These dependencies are crucial in knowledge modelling which appear as challenges in the current state of research due to inflexible database structures struggling to adapt to dynamic asset lifecycles, limiting comprehensive understanding and strategic decision-making. To address these challenges, this research proposes an innovative asset lifecycle event model using Labelled Property Graph (LPG) to integrate spatial, temporal, and semantic dependencies. The study develops a graph database approach enabling comprehensive asset lifecycle representation. The model was evaluated through lifecycle event queries across similar and diverse assets, examining temporal sequence retrieval and multidimensional asset dependencies analysis. Key findings demonstrate the framework’s ability to capture complex asset dependencies, support lifecycle visualisation and facilitate efficient data retrieval. Performance evaluation comparing Neo4j and PostgreSQL revealed the graph database approach outperformed traditional relational databases by 1.2 to 80 times across various query types. This research contributes to a novel conceptualization of integrating spatial, temporal, and semantic dependencies within a unified graph data structure in a graph database for asset management. In pursuit of comprehensive asset management for systematic decision-making, this approach introduces a novel data modeling method capable of storing multidimensional information interconnected with asset entities through graph database technology that advances the knowledge modelling within asset information by transforming the stored multidimensional data into comprehensive insights.
 
Keywords—graph databases, asset management, data modeling, information systems, spatio-temporal, knowledge management


Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).