Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making. To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities’ names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Bi-order-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF. Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.