Abstract
With the development of a new generation of information technology, such as big data and cognitive intelligence, we are in the postmodern era of artificial intelligence. Currently, the manufacturing industry is in the critical period of transitioning to smart manufacturing, but the cognitive capabilities of devices in smart factories are still scarce. Knowledge Graph (KG) is one of the key technologies of cognitive intelligence, which opens a new path for the horizontal integration of intelligent manufacturing. Therefore, this paper proposes and builds a manufacturing equipment information query system based on KG. Firstly, a large amount of heterogeneous data that contains vast devices information is obtained from the network. Secondly, the conditional random fields (CRF) algorithm is used to extract the entity name, product place, and company name of the device, and then the relationship between the device entities is identified by calculating the similarity and Chinese syntax analysis. In the validation section, we use to the map of Neo4j graph database, when we input a name of a device in the search box, the system can return a relational graph node. In addition, the shortest path optimization algorithm is used to calculate the similarity between nodes in the search process to achieve the recommendation of similar node information.
Highlights
The most fundamental purpose of Industry 4.0 is to transform traditional industrial manufacturing with advanced information technology [1], [2]
The above methods or models significantly improve the application and generalization of Knowledge Graph (KG) on product information, but few studies focus on manufacturing field
Based on the above research of Chinese Named entity recognition (NER), this paper focuses on the problem of naming recognition of intelligent manufacturing equipment in the Chinese corpus and selects a machine learning method based on conditional random fields (CRF) model for the NER of intelligent manufacturing equipment
Summary
The most fundamental purpose of Industry 4.0 is to transform traditional industrial manufacturing with advanced information technology [1], [2]. With the development and application of new technologies such as the Internet of Things (IoT) [7]–[9], cloud computing [10], big data, artificial intelligence [11], [12] and mobile communications [13], the horizontal integration of manufacturing information resources of smart factories has been greatly developed. Qi et al [14] proposed a supportive design and tools for scalable, modular integrated manufacturing integration and standardization. Their method effectively implemented enterprise application integration based on extensible language and Web service technology. With the development of intelligent manufacturing, a large amount of data is generated every day, and the semantics of data plays an important role in the extraction and application of manufacturing information
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