# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### KG Application  
The application of an enriched KG provides a wide range of analytical capabilities, leveraging the inherent structure and interconnected nature of graph data to generate actionable insights and improve decision-making. These capabilities encompass direct querying, integration with advanced AI models, graph-based analytical techniques, and causal inference, each of which offers unique advantages.  
The use of Cypher queries enables direct interaction with the KG. This allows users to retrieve specific information, identify patterns, and aggregate information efficiently. For example, in a KG representing the lifecycle of a product, Cypher queries can be used to pinpoint all suppliers involved in a particular production phase or to identify nodes with high centrality to reveal key intermediaries within the supply chain. By facilitating a targeted exploration of the graph, Cypher provides an essential mechanism for extracting actionable information from complex datasets. Another application is Graph RAG (Retrieval-Augmented Generation), which combines the structured retrieval capabilities of the graph with the generative power of language models. This integration enables the generation of more accurate, contextually aware responses in applications such as conversational AI or content creation. For example, in a system designed to address sustainability in manufacturing, Graph RAG can retrieve emissions-related data from the KG and incorporate them into a response to a user query, providing highly relevant and informed answers. This approach enhances the depth and quality of AI-driven systems by grounding generated content in reliable graph-based data. The application of Graph Data Science further extends the analytical utility of the KG by employing advanced algorithms to uncover structural and relational insights. Techniques such as community detection help identify tightly connected groups of nodes, which may represent clusters of suppliers or facilities that work closely together. Shortest path analysis, another key technique, can determine the most efficient routes or sequences between nodes, supporting tasks such as supply chain optimization or resource flow analysis. Centrality metrics, which evaluate the importance of nodes within the graph, are particularly useful for identifying critical entities or bottlenecks in complex processes. These tools allow organizations to uncover hidden patterns and relationships that may not be apparent through conventional analysis. Lastly, Causal AI adds a powerful layer of interpretability by moving beyond correlation to identify true cause-and-effect relationships within the data. Using the interconnected nature of the KG, causal inference can reveal dependencies and feedback loops, offering critical insights for decision-making. For example, it can help determine how changes in raw material sourcing impact energy consumption or how delays in one stage of production ripple through the supply chain to affect overall output. This capability enables scenario analysis and supports strategic planning by providing a deeper understanding of potential interventions and their consequences.  
Together, these applications transform the KG into a dynamic analytical tool. By enabling advanced querying, integrating contextual knowledge into AI systems, uncovering structural patterns, and identifying causal relationships, the KG becomes an invaluable resource for generating insights, supporting decision-making, and driving innovation.