# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 2 Theoretical Foundations and Related Works
### Knowledge Graphs
Frameworks like SAC-KG further simplify this process by automating entity recognition, relationship extraction, and validation, thus reducing the dependence on human supervision [31]. Since these knowledge graphs serve as the backbone of critical analytical processes, their overall quality, completeness, and alignment with source data become crucial factors in achieving their intended applications' benefits. Typically, the primary focus of the quality of a knowledge graph is its \"fitness for purpose\" [32] in relation to the application it supports. In the literature, several frameworks were introduced to control the quality of the KG. One study placed emphasis on improving the consistency of automatically constructed KGs [33]. Since such graphs could be generated through automated extraction tools, a notable number of errors and inconsistencies have been observed, particularly in terms of their alignment with the original source data. By leveraging LLMs, inconsistencies between extracted facts and their provenances were detected and corrected. In that study, decoder-only and encoder-decoder LLM architectures were systematically compared. It was demonstrated that smaller models can sometimes surpass larger ones in terms of performance, indicating that model size should not be regarded as the sole indicator of efficacy. Additional efforts were made to address the challenges posed by sparsity and noise within the KGs used for recommendation systems [34]. Long-tailed distributions and irrelevant or misleading connections have been identified as factors that degrade the quality of KG representations. To mitigate these issues, a Knowledge Graph Contrastive Learning (KGCL) framework was proposed. Within this framework, augmented KGs were produced so that noisy relationships could be suppressed, thereby enabling more robust and semantically meaningful entity representations. In [35], a method was proposed to support the efficient detection and explanation of inconsistencies in large-scale KGs. Traditional approaches have been found to become computationally prohibitive as graphs grow in size. To address this limitation, compact KG abstractions were introduced. In doing so, only crucial structural patterns were preserved, and pertinent inconsistencies were highlighted, reducing the computational overhead. Thus, error detection was rendered scalable, while accuracy and interpretability were maintained. A comprehensive framework was introduced to systematically assess the quality of large-scale KGs [32]. Authors claim that a significant number of KG have been developed through the use of automated construction tools and crowdsourcing methods. Within their evaluation framework, a set of metrics was defined to evaluate syntax, semantics, completeness, and scalability. By tailoring these metrics to diverse knowledge discovery applications, the complexity associated with evaluation protocols was reduced. Consequently, a more practical and scalable approach was offered to ensure KG quality. Finally, in [36], a survey of techniques for managing KG quality was presented. Processes such as error detection, error correction, and KG completion were examined in detail. Inaccuracies, outdated information, and incomplete entries were categorized, and a wide range of methodologies to systematically address these challenges were reviewed. As a result, a valuable resource was created for those seeking holistic strategies to enhance and maintain the overall quality of KG.