Construction of power projects knowledge graph based on graph database Neo4j

Liu, Haibo; Jiang, Guoyi; Su, Linhua; Cao, Yang; Diao, Fengxin; Mi, Lipeng

doi:10.1109/cits49457.2020.9232609

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…In the context of the era of big data, the relationship between the data to be processed increases geometrically with the amount of data [33]. For improved processing power, the ontology instances were implemented as a knowledge graph.…”

Section: Methodsmentioning

confidence: 99%

“…Neo4j handles nodes and relationships and allows efficient operations on graphs. According to [33], the relationship is the most important element of the database in Neo4j, as it represents the interconnection between nodes, which makes it ideal for representing knowledge graphs. The different programmes that make up the system were programmed with Python 3.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

2021

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Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

2021

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“…Liu et al used the Neo4j graph database to construct a typhoon disaster knowledge graph. This graph revealed the distribution patterns and the severity of typhoon disasters [53]. Therefore, knowledge graphs demonstrate immense potential in organizing and connecting multi-source disaster data, providing effective data and decision-making support for emergency situations.…”

Section: Related Workmentioning

confidence: 99%

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Zou,

Huang,

Wang

et al. 2024

Water

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Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

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“…W ITH the development of smart power grid, the requirements of analyzing and processing power big data are increasing. At present, lots of power metering information exists in discrete power subsystems, which makes it difficult to obtain effective knowledge for supporting decision-making [1]. It is challenging to integrate the discrete power metering data to build the power knowledge graph and provide effective guidance for supporting decision-making is an urgent problem.…”

Section: Introductionmentioning

confidence: 99%

Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

et al. 2021

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In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making. To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities' names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Biorder-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF. Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.INDEX TERMS power metering; entity recognition; Neural Network; pattern recognition;

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Construction of power projects knowledge graph based on graph database Neo4j

Cited by 8 publications

References 3 publications

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

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