A knowledge graph method for hazardous chemical management: Ontology design and entity identification

Zheng, Xue Lin; Wang, Bing; Zhao, Yunmeng; Mao, Shuai; Tang, Yang

doi:10.1016/j.neucom.2020.10.095

Cited by 60 publications

(18 citation statements)

References 24 publications

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“…Transportation safety of HCs has been a long-term concern and continuous research topic for many scholars because of its high social concern [ 17 , 18 ]. Accident analysis is an important tool that can be useful for improving and/or optimizing the safety level of HCRTS [ 19 ]. In the past five decades, a large number of studies have been conducted on accident analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China

Zhang

et al. 2021

IJERPH

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Safety of the hazardous chemicals road transportation system (HCRTS) is an important, complex, social, and environmental sensitive problem. The complexity, dynamics, and multi-link features of HCRTS have made it necessary to think beyond traditional risk analysis methods. Based on the relevant literature, Functional Resonance Analysis Method (FRAM) is a relatively new systemic method for modeling and analyzing complex socio-technical systems. In this study, a methodology that integrates FRAM, fuzzy sets, and risk matrix is presented to quantitatively assess the risks factors representing failure function links in HCRTS. As the strength of function links can be illustrated by the RI (risk index) of risk factors identified in failure function links, 32 risk factors representing 12 failure function links were first identified by accident causes analysis and the framework of FRAM. Fuzzy sets were then utilized to calculate the weight of the likelihood and consequence of the risk factors. Finally, according to the assessment results of the identified risk factors by a two-dimensional risk matrix, the weaker function links in the whole HCRTS chain were identified. HCs road companies, regulatory authorities, relevant practitioners, and other stakeholders should pay more attention to these links.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China

Zhang

et al. 2021

IJERPH

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“…Luo et al [8] identified chemical named entity by training an attention-based BiLSTM-CRF model. Zheng et al conducted named entity recognition for heterogeneous incidents records through pre-trained BERT-CRF model [9]. However, when dealing with the professional domain corpus, it is necessary to manually label the data sets before training or pre-training the neural network model which consumes a lot of labor costs.…”

Section: Introductionmentioning

confidence: 99%

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

et al. 2021

Lecture Notes in Electrical Engineering

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Knowledge graph, as a semantic network, provides a method to model knowledge from the perspective of entities and their relationships. Since a large quantity of material knowledge is widely distributed, using the structure of the knowledge graph is an effective approach to clarify the internal connections between related knowledge. Named entity recognition is a crucial step to construct a knowledge graph. However, most of the professional domain datasets are label-free and collected from multiple heterogeneous sources. To identify the entities from these datasets, a rule-based and deep learning hybrid method is proposed. The knowledge extracted by regular expression was used to semiautomatically label the corpus, and then trained by deep learning model, which achieved good results. The constructed knowledge graph regarding to the commonly used steels is of great significance to the integration and management of related knowledge, and it lays a foundation for future engineering application.

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“…Xia et al [9] proposed a method for establishing the knowledge graph of transformer operation and maintenance, which improved efficiency for knowledge utilization. Zheng et al [10] built a hazardous chemical management knowledge graph to optimize the utilization of text information in chemical documents.…”

Section: Introductionmentioning

confidence: 99%

Construction of Visual Question and Answering System Based on Knowledge Graph for Specific Objects

Dai

et al. 2021

Lecture Notes in Electrical Engineering

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As China's industry enters the 4.0 digital era, the segmentation of industrial applications is diversified. The knowledge of specific objects in the industrial scene is scattered that brings the pain points for management and operation. Knowledge graph (KG) has a powerful ability for relationship expression and real-time feedback that can integrate multi-source heterogeneous knowledge and provide data support for the Question Answering (Q&A) system. In this paper, a semi-automatic construction scheme for cupping machine knowledge graph is proposed and a visual Q&A system based on KG by the rule-based method is constructed. In the implementation of the KG, we design an ontology schema of the cupping machine and obtain entities and relationships from the network. This work solves the problem of the data island of cupping machines and provides a new technical method for the knowledge management of specific objects in the industrial equipment field.

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A knowledge graph method for hazardous chemical management: Ontology design and entity identification

Cited by 60 publications

References 24 publications

Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China

Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph

Construction of Visual Question and Answering System Based on Knowledge Graph for Specific Objects

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