# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 4 Construction of Access Control Model
### Ontology Construction Process  
Ontology is a formalized specification of a shared conceptual model that acts as a method for knowledge modeling. With the continuous deepening of ontology technology research, many mature ontology construction methods have emerged, including IDEF5, proposed in the 1970s; enterprise ontology [32], applied to enterprise ontology development; a more general method that combines the skeleton method and GOMEZ-PEREZ; methodology; and currently the most commonly used Stanford University seven-step method. Through learning and researching the methods of ontology construction, we have divided the construction of access control ontology into four main stages.  
1. Planning Stage The initial stage involves clearly defining the ontology's purpose and scope, focusing on capturing the essential knowledge of logistics business. On one hand, the necessary logistics business knowledge can be obtained through analysis and research on the logistics public information platform. On the other hand, the terminology and standardize concepts in logistics field is abstracted in logistics books and national logistics standards. Based on the concepts and terms specified in standards such as GB/T 18354-2001 National Standard of the National Standards of the People's Republic of China Logistics Terminology and GB/T Guidelines for the Application and Development of Logistics Public Information Platforms, this paper analyzes the concepts and attributes in logistics business, clarifies the knowledge structure and workflow in the logistics field, and then formulates the object, subject and environmental attributes for attribute-based access control within the logistics domain.
2. Design and Implementation Phase Following the planning stage, this stage is to classify knowledge and design access control ontology. This involves selecting an appropriate ontology description language and ontology modeling tool among 50 available tools such as JOE, KADS22, OILep, OntoEdit [33], Protege [34], Ontolingua [35], and WebOnto [36]. We here choose Protege due to its wide applicability, customizability, robust support community, and particularly because of its open-source nature and extensive documentation resources. With the optimal choice, we efficiently design and model the ontology, facilitating the definition of classes, relationships, attributes, and instances in a user-friendly environment.  
3. Evaluation Stage This stage involves assessing the constructed ontology against established ontology construction principles to ensure its validity and applicability. At first, the constructed ontology is evaluated in accordance with the principles of ontology construction. Then, the OWL inference engine checks and evaluates the consistency of the constructed ontology. Finally, we optimize and improve the ontology according to the feedback results, ensuring it accurately represents the logistics business collaboration semantics.
4. Maintenance Phase Ontology construction is not a one-time effort but requires ongoing maintenance to adapt to the evolving landscape of logistics knowledge. As the industry develops and new insights emerge, the ontology must be dynamically optimized and updated. This iterative process ensures that the ontology remains relevant and effective in modeling the knowledge base for the access control model.  
The construction and refinement of this ontology lay the groundwork for a semantic-based access control model tailored to the logistics industry's needs. By capturing the intricate relationships and attributes within logistics business collaborations, the model aims to facilitate nuanced and effective access control strategies, enhancing data security and collaboration efficiency within the industry.