DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning

Tiwari, Prayag; Zhu, Hongyin; Pandey, Hari Mohan

doi:10.1016/j.neunet.2020.11.012

Cited by 44 publications

(8 citation statements)

References 34 publications

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“…Augmenting the work of [167] they further develop the deep reinforcement learning framework by introducing three techniques, namely, reward shaping, action dropout, and force forward. Recently, a methodology called DA-Path [170] is proposed dependent on distance-based reward in the RL system to map variable awards for various positions. To allow the model to recall the path that considers overseeing the memory of relations in the path, it considers GSA (graph self-attention) with gated recurrent unit (GRU).…”

Section: Reinforcement Learning For Knowledge Graph Completionmentioning

confidence: 99%

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

Garg¹,

Roy²

2022

Preprint

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In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC) has emerged as a sub-domain of research to automatically track down the missing connections in a KG. Numerous strategies have been suggested to work out the KGC dependent on different representation procedures intended to embed triples into a low-dimensional vector space. Given the difficulties related to KGC, researchers around the world are attempting to comprehend the attributes of the problem statement. This study intends to provide an overview of knowledge bases combined with different challenges and their impacts. We discuss existing KGC approaches, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain knowledge graphs. In addition, reinforcement learning techniques are reviewed to model complex queries as a link prediction problem. Subsequently, we explored popular software packages for model training and examine open research challenges that can guide future research.

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Section: Reinforcement Learning For Knowledge Graph Completionmentioning

confidence: 99%

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

Garg¹,

Roy²

2022

Preprint

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“…Power information system integration is the product of the power system in the information age, and it is also an inevitable requirement for the power sector to participate in market competition. 1,2 However, the integration of heterogeneous power information systems requires the design of resources, information, and various hardware integration and functions for each subsystem. The workload is huge, a lot of money is needed, and the investment is relatively large, and some functions are more similar.…”

Section: Introductionmentioning

confidence: 99%

“…Under this operating mode, the integration of multiple heterogeneous power information systems will help the successful operation of each power information subsystem and make the entire power information system scalable, portable and maintainable. Power information system integration is the product of the power system in the information age, and it is also an inevitable requirement for the power sector to participate in market competition 1,2 . However, the integration of heterogeneous power information systems requires the design of resources, information, and various hardware integration and functions for each subsystem.…”

Section: Introductionmentioning

confidence: 99%

Data switching method among heterogeneous power information system databases based on knowledge graph

Bao

Tian

Gao

2022

Int J Numerical Modelling

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In order to solve the problems of long data switching time, low information utilization rate, and low switching accuracy in traditional data switching methods, a data switching method between heterogeneous power information system databases based on knowledge graphs is proposed. Based on the knowledge graph technology and its ternary structure, the data of the heterogeneous power information system is processed, and the N‐Gram model is used to realize the knowledge extraction, and the data error correction is established through the model. Through ODBC technology to complete the link between heterogeneous power information system databases, and then select the data access interface and determine the data extraction task, and finally realize the data exchange between the heterogeneous power information system databases. Experimental results show that the average communication channel bandwidth during the data switching process is 1.8 Gb/s, the highest value of data switching accuracy reaches 82%, and the information utilization rate after data switching is relatively high, indicating that the method has certain effectiveness.

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“…The rise of the digital world has driven up the consumer market for various applications [1][2][3][4][5][6][7][8][9][10][11][12][13]. Recently, haze removal has gained increased attention [14].…”

Section: Introductionmentioning

confidence: 99%

Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

Alenezi

Armghan

Mohanty³

et al. 2021

Water

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A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.

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DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning

Cited by 44 publications

References 34 publications

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

Data switching method among heterogeneous power information system databases based on knowledge graph

Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light

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