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.
H. Yan, J. Yang and J. Wan, “KnowIME: A System to Construct a Knowledge Graph for Intelligent Manufacturing Equipment,” in IEEE Access, vol. 8, pp. 41805-41813, 2020, doi: 10.1109/ACCESS.2020.2977136.
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