Remote supervision relation extraction method of power safety regulations knowledge graph based on ResPCNN-ATT

Sun, Jian; Zhao, Dezhi; Wang, Lei; Chen, Xiaoyu; Yi, Mingli; Xia, Lin

doi:10.1109/icpeca51329.2021.9362720

Cited by 2 publications

(1 citation statement)

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“…The parameter sharing approach is to model entities and relations separately and to realize the interaction between 2 subtasks by sharing a joint coding layer for joint learning [9], but there is still the problem that redundant entity information cannot be eliminated. Therefore, some scholars [10] [11] have studied to transform the joint extraction of entity relations into a sequence tagging problem, which solves the entity redundancy as well as overlapping relations problem to some extent. Literature [12] carefully analyze the key techniques and methods of the construction of insect pest and disease knowledge graph in recent years based on the characteristics of insect pest and disease data, and therewith conclude that ontology learning, machine learning, and deep learning are the key techniques to achieve automatic knowledge extraction, and are also the current research hotspots of plant insect pest and disease based on knowledge graphs.…”

Section: Introductionmentioning

confidence: 99%

DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

Liu¹

2021

IEEE Access

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In this study, a method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed. Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference. Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data. In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction. In addition, DKG-PIPD used a novel end-toend model to train and predict on the crawled dataset. The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method. Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced. Finally, the improvement direction of this paper was also introduced in the discussion section.

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Section: Introductionmentioning

confidence: 99%

DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

Liu¹

2021

IEEE Access

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A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

Liu

et al. 2023

Applied Sciences

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Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.

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Remote supervision relation extraction method of power safety regulations knowledge graph based on ResPCNN-ATT

Cited by 2 publications

References 10 publications

DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

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