# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### Synthetic Data Integration  
While the described data integration enables the integration of available data from various databases into a KG, it is important to recognize that relevant data for every entity may not always be accessible through such databases. In many cases, especially for specific or novel products, detailed environmental data, such as precise CO\({}_{2}\) emission values, are not readily available. Traditionally, obtaining this information would require a direct engagement with production facilities to manually record and measure the necessary data. This process involves on-site assessments, data collection from manufacturing processes,and possibly confidential information sharing, which can be time-consuming and resource-intensive. However, this traditional approach is impractical during the product design phase, a stage characterized by rapid iteration and frequent changes. Designers and engineers need immediate access to data to make informed decisions, evaluate different design options, and optimize for sustainability metrics. Waiting for precise data collection from production facilities would significantly slow down the design process and hinder innovation, and it may not be feasible due to the dynamic nature of product development. To address this challenge, we propose leveraging the semantic knowledge embedded in advanced language models to estimate missing values. Language models like GPT-4 have been trained on extensive corpora of text data, which cover a wide range of topics, including materials science, engineering, and environmental studies. These models capture complex relationships and patterns within the data, enabling them to generate plausible estimates based on contextual understanding. By entering known information about a product, such as its material composition, manufacturing processes, and intended use, the model can infer missing environmental data by relating it to similar known entities. The models are able to understand the semantic relationships between concepts, recognizing that certain materials have typical properties or environmental impacts based on their composition and production methods. Furthermore, these models can perform analogical reasoning, comparing the unknown entity to similar entities with available data. For example, if CO\({}_{2}\) emission data for a specific type of biodegradable plastic is missing, the model can estimate it by analyzing data from similar plastics with known properties. Advanced language models have been exposed to scientific literature, industry reports, and environmental assessments, allowing them to synthesize this information and provide estimates that align with existing scientific understanding and empirical data trends. The goal is for designers to be able to explore a wider range of materials and design options, even when complete data are not available, providing flexibility in the creative process. Early-stage estimates enable preliminary assessments of environmental impacts, guiding design choices towards more sustainable options from the outset. However, there are limitations to and considerations for this approach. While language models can provide valuable estimates, they may not always match the precision of empirically collected data. It is important to validate model-generated estimates with actual data when they become available to ensure accuracy and reliability. Additionally, care must be taken to ensure that the use of language models does not inadvertently disclose proprietary information or violate data privacy regulations, addressing ethical and confidentiality concerns. The estimated data generated via language models can be incorporated into the KG with appropriate annotations indicating the source and confidence level. This integration enhances the KG's completeness, allowing for more comprehensive analyses and queries. Over time, as actual data become available, it can replace or refine the estimated values, improving the KG's accuracy.