# Research on Driving Scenario Knowledge Graphs
## 2 Knowledge Graph  
The concept of a knowledge graph [8] was formally introduced by Google in 2012, aiming to depict various concepts, entities, and their relationships in the real world using graph theory. A knowledge graph is composed of entities, relationships, and facts, formalized as \(G=(E,R,T)\). Here, \(E\) represents the set of entities, \(R\) represents the set of relationships, and \(T\) represents the set of triples \((h,r,t)\in T\), where \(h\in E\) denotes the head entity, \(t\in E\) denotes the tail entity, and \(r\in R\) denotes the relationship. In this structure, entities are interconnected through relationships, forming a networked knowledge system. Compared to traditional text and tabular representations, knowledge graphs, with their intuitive and efficient characteristics, can clearly display entity attributes and their connections, thereby enabling tasks such as intelligent search, question reasoning, and recommendations [9; 10; 11]. Furthermore, knowledge graphs often utilize a resource description framework (RDF) and web ontology language (OWL) to represent and define the structure of the data, thereby enhancing interoperability and reasoning capabilities by facilitating data exchange and specifying concepts, relationships, and constraints within a domain.  
The construction of a knowledge graph involves two main layers: the schema layer and the data layer [12]. The ontology, as the schema layer of the knowledge graph, constrains the data layer through rules and axioms defined by the ontology. The data layer, situated below the ontology layer, stores information in the form of triple tables, attribute tables, etc., in a graph database. For the schematic diagram of a domain knowledge graph structure, please refer to Figure 1.  
The construction of a knowledge graph can be mainly divided into two categories: top-down and bottom-up approaches [13]. The top-down approach establishes the schema layer first, guiding the construction of the data layer, directly extracting ontology and schemas from structured data sources, and incorporating them into the knowledge base. This method performs well in displaying hierarchical relationships between concepts but requires a high degree of human involvement and has limited schema layer updates, making it more suitable for constructing knowledge graphs with smaller scales and clear hierarchical structures. If a domain knowledge graph has a small scale and a clear knowledge system, the top-down approach can be considered. The bottom-up approach, on the other hand, first constructs the data layer, integrates the acquired knowledge, extracts and filters ontology patterns from the data, and then manually selects new patterns with high confidence to add to the knowledge base. This method updates rapidly and is suitable for large-scale knowledge graph construction but may contain more noise. Typically, the bottom-up approach is chosen for constructing general-purpose knowledge graphs due to the richness and complexity of general knowledge, which makes defining patterns manually challenging.  
To balance the advantages of both approaches, we adopted a hybrid knowledge graph construction approach. The overall process is illustrated in Figure 2. Firstly, core knowledge is collected from the public domain of driving scenarios, and the domain concept system is summarized to construct the top-level ontology of the multidimensional hierarchical knowledge graph. Secondly, knowledge extraction is performed using massive structured data to extract entities, attributes, and relationships, forming a small-scale graph structure relevant to new knowledge. Next, the schema layer is integrated to construct the knowledge graph for intelligent connected vehicle driving scenarios. Subsequently, based on existing data in the knowledge graph, knowledge embedding reasoning is conducted, and the quality of the reasoning results is evaluated to expand and enrich the knowledge graph.  
Figure 1: Conceptual representation of knowledge graph structure.