# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 2 Theoretical Foundations and Related Works
### Knowledge Graphs  
KGs represent a structured approach in the realm of data management, offering a unique way to encapsulate relationships within a set of triples [17]. They serve as powerful tools for bridging the gap between vast data availability and the extraction of actionable insights, particularly in domains that require comprehensive understanding and decision-making. The graph structure of KGs enables the representation of complex relationships and interdependencies among data entities, facilitating advanced analytics and knowledge discovery. In 2016, Google advanced the conceptual framework of KGs, laying the foundation for contemporary discussions. They utilized KGs to improve and accelerate their search engine results, aligning with the broader ambition of evolving the Internet into a machine-readable \"web of data\" [18]. KGs have increasingly become integral across various domains, demonstrating their versatility and utility. In biomedical research, they facilitate knowledge extraction with minimal supervision, aiding in the discovery of new insights and relationships within vast datasets [19]. In cybersecurity education, KGs are used to visualize complex concepts, enhancing learning outcomes by providing clear and interconnected representations of information [20]. They play a crucial role in drug discovery as well, helping to quickly identify candidates for repurposable drugs, as demonstrated during COVID-19 research [21]. In supply chain risk analysis, KGs support decision-making processes by integrating heterogeneous data from multiple sources, allowing for a more holistic assessment of risks [22]. Today, there are mainly two types of KGs [23]. First, labeled property graphs (LPGs) are renowned for their efficient storage, rapid traversal capabilities, and flexibility in modeling a wide array of real-world domains. These attributes make LPGs particularly advantageous for applications that require fast query processing and deep graph analyses, such as subgraph matching, network alignment, and real-time KG queries. LPGs are characterized by their structure, where edges are used to connect nodes that contain embedded properties [24]. This design leads to significant efficiency in data querying and analysis. However, despite these benefits, LPGs lack support for formal knowledge representation, such as ontologies, limiting their applicability in scenarios where automatic knowledge inference is crucial. Secondly, the Resource Description Framework (RDF) serves as a versatile data model engineered for sharing information concerning entities in the Web of Data. Its robustness is demonstrated through the facilitation of formal knowledge representations like ontologies, enabling the automated inference of knowledge [25]. RDF employs a triplet structure consisting of a subject, a predicate, and an object, facilitating the linking of data and its sharing across the web. This makes it ideal for applications where data linking and sharing are paramount. Although RDF is widely adopted within the web community, its tendency towards a high memory footprint and inefficient storage can impede scalable graph analyses, an area where LPGs offer advantages. In LPGs, properties are stored directly on nodes and edges, rather than being relegated to external schemas or global ontologies. This flexibility facilitates efficient data storage, rapid graph traversal, and the versatile modeling of real-world domains where heterogeneous attributes can be directly associated with elements of the graph structure. However, this comes at the cost of limited support for formal reasoning or inference based on logical frameworks or ontologies. Throughout this study, when we use the term _knowledge graph_, we specifically refer to the labeled property graph model as defined here, due to its efficiency and flexibility in handling the complex and extensive data needs of life cycle assessments.