Question answers technology towards maintenance of CNC machine tools

被引：0
|
作者：
Bei Y. [1 ]
Zhou Y. [1 ]
Qao K. [1 ]
机构：
[1] School of Software Technology, Zhejiang University, Ningbo
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 09期
关键词：
attention mechanism; CNC machine tools; knowledge embedding; knowledge graph completion; knowledge question answering;
D O I：
10.13196/j.cims.2022.09.019
中图分类号：
学科分类号：
摘要：
In recent years, knowledge-based reasoning technology has been widely used in many fields, but the research in the field of CNC machine tool equipment maintenance is relatively scarce. From the perspective of knowledge reasoning, combined with the scattered and incomplete CNC equipment maintenance data, a new knowledge graph completion method based on attention mechanism was proposed, which coupled with CNN and BiGRU that named ConvBiGRU modules. ConvBiGRU module mainly encoded multiple inference paths between entities as low-dimensional embedding, and used the attention mechanism to capture the semantic correlation between candidate relationship and each path between two entities. The Embedding method was adopted to realize the multi-step knowledge question and answer in the field of CNC machine tool equipment maintenance. ComplEx was used to embed the knowledge graph of CNC machine tool equipment maintenance, while RoBERTa model was used to embed the user problems. The superiority of the proposed method in the field of CNC machine tool had been verified from many-ways. © 2022 CIMS. All rights reserved.
引用
收藏
页码：2881 / 2893
页数：12
相关论文
共 19 条
  • [1] ZHOU Yang, LI Qing, Ontology modeling and semantic retrieval for aircraft fault knowledge, Computer Engineering and Applications, 47, 16, pp. 12-15, (2011)
  • [2] QIN Dali, Research on intelligent diagnosis for equipment fault based on knowledge management [J], (2014)
  • [3] ZHANG Yong, YAN Junweu, GAO Yan, Et al., Knowledge model for continuous casting equipment maintenance repair and overhaul [J], Computer Integrated Manufacturing Systems, 16, 12, pp. 2708-2714, (2010)
  • [4] DASR, DHULIAWALA S, ZAHEER M, Et al., Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
  • [5] DAS R, NEELAKANTAN A, BELANOER D, Et al., Chains of reasoning over entities, relations and text using recurrent neural networks [EB/OL]
  • [6] JIANG X T, WAMG Q, QI B Y, Et al., Attentive path combination for knowledge graph completion
  • [7] BORDES A, USUNIER M, GARCIA-DURAN A, Et al., Translating embeclclings for modeling multi-relational data, Proceedings of the 26th International Conference on Neural Information Processing Systems, 2, pp. 2787-2795, (2013)
  • [8] LIN Y K, LIU Z Y, SUN M S, Et al., Learning entity and relation embecldings for knowledge graph completion, Proceedings of the 29th AAAI Conference on Artificial Intelligence, (2015)
  • [9] HILDEBRANDT M, SERNA J A Q, MA Y P, Et al., Reasoning on knowledge graphs with debate dynamics [EB/OL]
  • [10] JAGVARAL B, LEE W K, ROH J S, Et al., Path-based reasoning approach for knowledge graph completion using CNN-BiLSTM with attention mechanism, Expert Systems with Applications, 142, (2020)
12