GRL: Knowledge graph completion with GAN-based reinforcement learning

Wang, Qi; Ji, Yuede; Hao, Yongsheng; Cao, Jie

doi:10.1016/j.knosys.2020.106421

Cited by 36 publications

(12 citation statements)

References 18 publications

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“…In recent years, with the development of Generative Adversarial Network technology, GAN combined with other deep learning technologies has more and more applications in data processing Yang et al [12], Wang et al [13], Yu et al [14], which has solved many problems. In the eld of job shop scheduling, there is no well-established and mellow technology to solve the problem of missing scheduling data.…”

Section: Related Workmentioning

confidence: 99%

Repairing of Missing Data in Scheduling Based on Generative Adversarial Network

Chen

Song

et al. 2022

Preprint

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In the manufacturing process of modern smart factories, when product family design and variant design are carried out on the basis of existing products, local design changes will generate new technological processes. Process changes lead to partial lack of scheduling data, which in turn affects the efficiency of workshop scheduling in the manufacturing process. most of the research results related to scheduling problems are focused on the optimization of various scheduling objectives, without considering the problem of missing scheduling data. Therefore, this paper proposes a data processing method that combines missing data removal and data expansion based on processing order for optimal training of GAN to solve the problem of insufficient training data when the scheduling data set is small. In this paper, We use GAN to generate local data, and combine the missing vector of the data and the hint vector to strengthen the training process, complete the repair of the missing data, and then further study the effectiveness of the repair. Experimental results show that this method of using historical process data to repair scheduling data can effectively repair the problem of partial missing data in the same product family and improve the efficiency of workshop production scheduling.

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Section: Related Workmentioning

confidence: 99%

Repairing of Missing Data in Scheduling Based on Generative Adversarial Network

Chen

Song

et al. 2022

Preprint

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“…The enterprise risk domain knowledge map service displays enterprise risk domain knowledge in an interactive and visual way by analyzing the evolutionary law and correlation characteristics of risk events [ 35 ]. The enterprise risk domain knowledge map service can describe the hierarchical structure of the risk domain knowledge and the semantic connections between the risk event knowledge and intuitively display the organizational structure and relevance of the risk domain knowledge so that the enterprise risk manager can quickly obtain the required risk domain knowledge, thereby promoting the association and sharing of knowledge in the field of enterprise risk.…”

Section: The Construction Of a Knowledge Service Model Driven By Risk Eventsmentioning

confidence: 99%

Research on enterprise knowledge service based on semantic reasoning and data fusion

Yang

Meifang

2021

Neural Comput & Applic

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In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low level of intelligence in risk management, this article explores the application process of enterprise knowledge service models for rapid responses to risk incidents from the perspective of semantic reasoning and data fusion and clarifies the elements of the knowledge service model in the field of risk management. Based on risk data, risk decision making as the standard, risk events as the driving force, and knowledge graph analysis methods as the power, the risk domain knowledge service process is decomposed into three stages: prewarning, in-event response, and postevent summary. These stages are combined with the empirical knowledge of risk event handling to construct a three-level knowledge service model of risk domain knowledge acquisition-organization-application. This model introduces the semantic reasoning and data fusion method to express, organize, and integrate the knowledge needs of different stages of risk events; provide enterprise managers with risk management knowledge service solutions; and provide new growth points for the innovation of interdisciplinary knowledge service theory.

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“…The basic different characteristics are used as the weight value of transmission intensity. Furthermore, using the structural information from the music recommendation KG [21] to carry out iterative calculation through the user preference transmission model combined with the transmission intensity, the structural features are extracted and the training model is used as input to adjust the importance of different features through the objective function to achieve the optimal result. The entities are sorted and taught to generate top N recommendation lists, and then the click rate is predicted.…”

Section: St_ripplenet Modelmentioning

confidence: 99%

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

Yang¹,

Cheng²

2021

IJCIS

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In recommendation algorithms, data sparsity and cold start problems are inevitable. To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance. In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed. This model starts by building the required knowledge graph. Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities. In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability. Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information. In the ST_RippleNet model, the music data set is added to the movie and book data set; additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer. Finally, the tanh function is added to predict the click probability to improve recommendation performance. Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations.

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GRL: Knowledge graph completion with GAN-based reinforcement learning

Cited by 36 publications

References 18 publications

Repairing of Missing Data in Scheduling Based on Generative Adversarial Network

Repairing of Missing Data in Scheduling Based on Generative Adversarial Network

Research on enterprise knowledge service based on semantic reasoning and data fusion

Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

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