Abstract
The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.
Highlights
Human beings understand the environment and recognize the objects in the environment according to the shape and color of the objects, and the information of environment and objects acquired by human beings is stored in the human brain in a structured and hierarchical form
To solve the problem of entity semantics inconsistent, this paper proposes an entity semantic fusion method, which calculates its word vectors through text information and description information of structured knowledge and determines the similarity of entity semantics according to the cosine similarity of word vectors, realizing the alignment and disambiguation of entity semantics
Work is paper mainly studies how to construct the representation model of environmental information in industrial Internet, proposes a representation model of cognitive information based on knowledge graph, gives the construction process of the model, and describes the detailed construction of the perception layer and cognition layer
Summary
Human beings understand the environment and recognize the objects in the environment according to the shape and color of the objects, and the information of environment and objects acquired by human beings is stored in the human brain in a structured and hierarchical form. E application of knowledge graph technology to the construction of robot knowledge base in industrial Internet can greatly improve the ability of the robot to represent and store environmental knowledge. It can effectively unify various environmental information into context information that the robot can understand. The knowledge base based on knowledge graph stores environmental information in the form of structured network, which makes the robot knowledge base query have similar associative ability to human beings and becomes the key to improve the robot’s intelligence and realize service tasks
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