# 6G Cognitive Information Theory: A Mailbox Perspective
## 8 Key Technology for Information Cognitive
### Extraction of Information Value Based on Knowledge Graph
The symbol-based seasoning is generally used to apply seasoning rules to the knowledge graph so as to derive new entity relations by triggering the antecedent of rules. In this work, seasoning rules are considered to be owned by knowledge seasoning language, set by humans, or obtained by a machine learning technique. Although symbol-based seasoning includes all types of optimization methods to improve seasoning efficiency, it cannot keep pace with the speed of data growth, especially when the data size is beyond the handling capacity of the memory-based server. To cope with this challenge, researchers combined description logic and seasoning to improve the efficiency and expandability of seasoning, and many achievements have been realized. For instance, the CloudSEC is a real-time lateral movement test method that was proposed for the evidence seasoning network of the edge cloud environment. Statistics-based seasoning can be divided into learning based on the entity relationship and induction based on models. The learning based on entity relationships is to learn the relationship between entities in the knowledge graph using statistical methods or neural network methods and has been studied extensively. The related research can be roughly divided into two categories: methods based on representation learning and methods based on graph characteristics. The methods based on representation learning map entities and relations in the knowledge graph jointly combine into a low-dimensional continuous vector space to depict their potential semantic features. By comparing and matching the distributed representations of entities and relations, relations between entities in the knowledge graph can be established, and a possible edge can be predicted using the graph features observed in the knowledge graph by the graph-features-based method. This method can explore seasoning rules automatically from the knowledge graph and show a clear seasoning mechanism. The model induction method is to learn the model-level information of the thing-in-itself from the knowledge graph or to enrich the existing thing-in-itself, including the concept hierarchy and attribute hierarchy. For instance, a knowledge bridge-map network model has been proposed. This model bridges the cross-pattern semantic relations between vision and text knowledge via the graph and with fine granularity and searches for the required knowledge via the self-adaptive information selecting model. Based on the above descriptions, it can be concluded that the knowledge graph cannot only describe the multi-dimensional features of entities, but can also reinforce the comprehension of information in view of its rich content of semantic information. Therefore, it has been widely used in personalized recommendations, information searching, and smart question and answer systems. Take the recommendation system as an example. In this system, entities can be searched via the knowledge graph based on a user's preference and relations of things in order to make things match the users' preference. So, the knowledge graph is a potential technology capable of cognizing information. Moreover, it should be noted that in the mailbox theory, different applications need to acquire the data of specific domains to extract and transmit data, and the shift learning based on knowledge graph can be used to realize the shift between different entities. Thus, it is clear that using the knowledge graph shows potential advantages in cognizing information.