Affiliation:
1. Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Abstract
With the development of intelligentization in maritime vessels, the pursuit of an organized and scalable knowledge storage approach for marine engine room systems has become one of the current research hotspots. This study addressed the foundational named entity recognition (NER) task in constructing a knowledge graph for marine engine rooms. It proposed an entity recognition algorithm for Chinese semantics in marine engine rooms that integrates language models. Firstly, the bidirectional encoder representation from transformers (BERT) language model is used to extract text features and obtain word-level granularity vector matrices. Secondly, the trained word embeddings are fed into a bidirectional long short-term memory network (BiLSTM) to extract contextual information. It considers the surrounding words and their sequential relationships, enabling a better understanding of the context. Additionally, the conditional random field (CRF) model was used to extract the globally optimal sequence of named entities in the marine engine room semantic. The CRF model considered the dependencies between adjacent entities that ensured a coherent and consistent final result for entity recognition in marine engine room semantics. The experiment results demonstrate that the proposed algorithm achieves superior F1 scores for all three entity types. Compared with BERT, the overall precision, recall, and F1 score of the entity recognition are improved by 1.36%, 1.41%, and 1.38%, respectively. Future research will be carried out on named entity recognition of a small sample set to provide basic support for more efficient entity relationship extraction and construction of a marine engine room knowledge graph.
Funder
project Development of Liquid Cargo and Electromechanical Simulation Operation System for LNG Ship
National Key R&D Program of China
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference25 articles.
1. Duhaney, J.A. (2012). Mining and Fusing Data for Ocean Turbine Condition Monitoring. [Ph.D. Thesis, Florida Atlantic University].
2. Gao, M., Shi, G., and Li, S. (2018). Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network. Sensors, 18.
3. Pan, J.Z., Vetere, G., Gomez-Perez, J.M., and Wu, H. (2017). Exploiting Linked Data and Knowledge Graphs in Large Organizations, Springer International Publishing. [1st ed.].
4. Discovery and Disambiguation of Entity and Relation Instances;Maggini;IEEE Trans. Neural Netw. Learn. Syst.,2020
5. A review: Development of named entity recognition (NER) technology for aeronautical information intelligence;Baigang;Artif. Intell. Rev.,2023