# 6G Cognitive Information Theory: A Mailbox Perspective
## 8 Key Technology for Information Cognitive
### Extraction of Information Value Based on Knowledge Graph  
A knowledge graph can be used to find delivered information and realize the conversion from information to knowledge. To be specific, a knowledge graph is used to describe objective things in the real world by using a graphic structure consisting of many nodes and edges. Nodes denote entities (namely, concrete things) or concepts (namely, abstract things), while edges represent internode relations that connect things or concepts. Particularly, nodes can save the value of their own attributes, which are used to record the internal characteristics of things, while the inter-edge relations are used to reflect the external connection of things. In the knowledge graph, the triple of node-relation-node can be deemed as a data record, where the first node represents a subject, the edge denotes the predicate, and the end node stands for an object. The subject, predicate, and object constitute a record, and the set made of massive records includes the knowledge graph. Generally, the transformation process from the original data to the knowledge graph includes steps of knowledge acquisition and extraction, knowledge fusion (including entity dis-ambiguity and anaphora resolution), knowledge representation, and knowledge reasoning. Knowledge acquisition is used to acquire knowledge about related entities and relations from different sources [70]. Particularly, the original data constituting the knowledge graph generally come from three sources: structured data, semi-structured data, and non-structured data. Different knowledge acquisition methods can be adopted for different sources. Structured data are stored in a relational database and have a certain data structure and relation name and inter-project correspondence, so it is needed only to convert the data into the resource description framework (RDF). The semi-structured data generally have a certain data structure but need to be further extracted and organized due to irregular formats. For instance, for web data, specific text contents can be extracted from web data and stored as structured data. Knowledge acquisition of non-structured data can be divided into three steps generally: (i) entity extraction or entity identification. Here, an entity represents a concept, thing, human, or place name in the objective world. (ii) Relation extraction, which is to analyze the relation of entities based on the text data using multiple techniques; (iii) Attribute extraction, which is to identify the attribute information of an entity or relation. As for the identification of an entity, traditional algorithms mainly rely on statistical models. Today, many algorithms are using LSTM to realize the further acquisition of semantic information. As for relation extraction, automatic extraction of specific semantic relations between different entities can be realized to compensate for missing relations in the graph. However, an error can easily be made when the aforementioned extraction methods are used. To solve this problem, an end-to-end neural network model has been proposed to identify entities and extract relations and attributes. Figure 11: The cognitive information. Knowledge fusion is to recognize the same entity from different knowledge bases and thus solve the integration problem of multi-source heterogeneous data. Particularly, the same entity will most likely show different attributes and relation descriptions for different knowledge bases, and two knowledge bases may differ from each other in all aspects. Knowledge fusion is used to unify multiple descriptions of an entity to obtain complete information about the entity. The fusion process generally includes extraction and fusion of things-in-itself, entity alignments, entity linking, and attribute fusion. Entity alignment is the core of knowledge fusion. It needs to compute the similarities between entities by methods that include clustering and knowledge embedding. In these methods, knowledge embedding is used extensively.