# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 2 Theoretical Foundations and Related Works
### Related Works and Research Gap  
Several KG-based models have been developed to improve LCA. To date, the main focus of research has been the topic of the life cycle inventory (LCI). An LCA-oriented KG development methodology was introduced by [37] to improve LCI management. In their approach, the semantic representation of LCI data was emphasized, facilitating better data integration and retrieval. By structuring LCI information into a KG, a foundation was provided for more sophisticated analysis and decision-making processes within LCA. The frameworks developed for LCI by [38; 39] also utilize knowledge graphs to improve the LCI. The KGs integrate and organize heterogeneous data sources, enabling the automated generation of material and energy flow data for either manufacturing processes [38] or buildings and infrastructure [39]. The approaches enhance data traceability and interoperability, ensuring a robust foundation for comprehensive environmental assessments. Similarly, a consensus model for LCA leveraging semantic catalogs to enhance data sharing and reuse was developed by [40]. Their work focused on creating a standardized semantic framework that improves the efficiency and applicability of LCA data. By adopting semantic technologies, the challenges of data heterogeneity and interoperability were addressed, enabling a more seamless integration of LCA datasets from diverse sources. A semantic approach to modeling LCI databases was proposed by [41], specifically aimed at the management of energy and environmental impact data. In their methodology, the use of ontologies was involved in the semantic representation of LCI data, making the information more accessible and comprehensible. This approach not only improves data retrieval and analysis but also supports the integration of LCI data into broader environmental assessment frameworks. Building upon these foundational works, a comprehensive KG using Neo4j for the management of LCI data was presented by [23], marking a significant shift from traditional relational databases. Their methodology involves the construction of a KG comprising more than 40 million entities and 100 million relationships. By conceptualizing LCI data through interconnected concepts and relationships, a more intuitive and flexible data model was provided. The preprocessing of data for Cypher syntax extraction enabled efficient querying and data manipulation within the Neo4j environment. Improved data visualization and retrieval capabilities were demonstrated in their implementation, highlighting the advantages of KGs over conventional database systems in handling complex LCA data structures. Beyond LCI data management, a study presents a knowledge graph-based model for analyzing and managing the carbon footprint of a product [42]. The proposed model leverages semantic technologies to integrate heterogeneous data sources and represent the complex relationships between materials, processes, and emissions. It can be demonstrated how the knowledge graph facilitates efficient data organization and dynamic updates, addressing key challenges in traditional LCA methods. The findings underscore the importance of combining semantic data representation with LCA to address the growing complexity of environmental impact assessments. However, specific details regarding the technological framework and software used remain limited. More research is needed to evaluate scalability and applicability to other industries and especially environmental factors, broadening its utility beyond carbon footprint analysis. More recent research has further advanced the application of KGs in LCA. A knowledge graph-based methodology tailored for proactive circularity was proposed for the disassembly of smart products, incorporating principles of circular economy [43]. This framework integrates design for circular disassembly (DfCD) concepts into the product lifecycle, providing dynamic indicators for economic, environmental, and social contexts.