Abstract
With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.
Highlights
In recent years, traditional coal mines (CMs) have been developing gradually toward informatization, automation, and intelligence [1]. e essence of CM intellectualization is that, from development design to production management, the main CM systems should have the basic abilities of selfperception, self-learning, self-decision-making, and selfexecution [2]. e concept of intelligent CMs is aligned with the technological revolution currently taking place in the coal industry, and it provides core technical support for high-quality development of that industry [3, 4]
Bidirectional Encoder Representations from Transformers (BERT) is introduced into the bidirectional long short memory network (BiLSTM) combined with a conditional random field (CRF), and a named-entity recognition model based on BERT-BiLSTM-CRF is constructed. e BERTBiLSTM-CRF model transforms the entity recognition task directly into a sequence annotation problem; that is, by constructing the BERT-BiLSTM-CRF sequence annotation model, the model structure is as shown in Figure 7. e sequence annotation model has five layers: (i) the input layer, (ii) the BERT layer, (iii) the BiLSTM layer, (iv) the CRF layer, and (v) the output layer. e input layer represents the input sequence to be labeled, and the output layer represents the labeled sequence
Based on the evaluation system, we introduce precision, recall, F1 (F-measure), and accuracy to analyze the entity recognition results of each model. e experimental results are given in Table 2 and show that, for the recognition effect of CME maintenance (CMEM) entities in the same data set, the recognition of the BERT-BiLSTM-CRF model is superior to that of the Word2ve-long short-term memory (LSTM) model in terms of accuracy, recall, and F1 value
Summary
Received 5 August 2021; Revised 11 October 2021; Accepted 27 October 2021; Published 10 November 2021. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. En, aiming at the problem that the traditional namedentity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. Through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system
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