Step by step: A hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

Zhu, Anjie; Ouyang, Deqiang; Liang, Shuang; Shao, Jie

doi:10.1016/j.knosys.2022.108843

Cited by 26 publications

(4 citation statements)

References 27 publications

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“…Notably, multi-hop reasoning on massive knowledge graphs is one of the challenging tasks (Zhu et al. 2022 ). For instance, most recent studies focus on multi-hop reasoning over knowledge graphs, which have only 63K entities and 592K relations.…”

Section: Technical Challengesmentioning

confidence: 99%

Knowledge Graphs: Opportunities and Challenges

et al. 2023

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With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.

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Section: Technical Challengesmentioning

confidence: 99%

Knowledge Graphs: Opportunities and Challenges

et al. 2023

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“…Gradient Rollback [48], RNNLogic [76], CPL [30], GPFL [31], CAKE [64], R2D2 [36], RED-GNN [139], HiAM [58], SparKGR [124], [65], [142], RuleGuider [51], RPJE [66], RuleDict [135], [10], NTPs [79], SQUIRE [6], LCGE [63] Global post-hoc…”

Section: Entity Extraction Relation Extraction Entity Resolution Link...mentioning

confidence: 99%

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Zhang

Meroño‐Peñuela

Simperl

2023

Frontiers in Artificial Intelligence and Applications

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Knowledge graphs are important in human-centered AI because of their ability to reduce the need for large labelled machine-learning datasets, facilitate transfer learning, and generate explanations. However, knowledge-graph construction has evolved into a complex, semi-automatic process that increasingly relies on opaque deep-learning models and vast collections of heterogeneous data sources to scale. The knowledge-graph lifecycle is not transparent, accountability is limited, and there are no accounts of, or indeed methods to determine, how fair a knowledge graph is in the downstream applications that use it. Knowledge graphs are thus at odds with AI regulation, for instance the EU’s upcoming AI Act, and with ongoing efforts elsewhere in AI to audit and debias data and algorithms. This paper reports on work in progress towards designing explainable (XAI) knowledge-graph construction pipelines with human-in-the-loop and discusses research topics in this space. These were grounded in a systematic literature review, in which we studied tasks in knowledge-graph construction that are often automated, as well as common methods to explain how they work and their outcomes. We identified three directions for future research: (i) tasks in knowledge-graph construction where manual input remains essential and where there may be opportunities for AI assistance; (ii) integrating XAI methods into established knowledge-engineering practices to improve stakeholder experience; as well as (iii) evaluating how effective explanations genuinely are in making knowledge-graph construction more trustworthy.

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“…Multiple possible HR images, however, can restore a single LR image due to the ill-posed nature of SR. To alleviate this issue, many SR models have been presented such as interpolation-based, 1 reconstruction-based, 2 and learning-based methods. 3,4 Recently, convolutional neural networks (CNN) have huge advantages in computer vision, [5][6][7] including the SR domain. Dong et al 8 innovated a three-layer CNN for the SR task and obtained good performance, called SRCNN.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, convolutional neural networks (CNN) have huge advantages in computer vision, 5 – 7 including the SR domain. Dong et al 8 .…”

Section: Introductionmentioning

confidence: 99%

Dual-aware transformer network for single-image super-resolution

Luo

Wang

et al. 2023

J. Electron. Imag.

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The advent of deep learning has enabled image super-resolution (SR) technology to achieve tremendous success. The recent exploration between convolutional neural network (CNN) and transformer has produced an impressive performance. However, the huge model size and heavy computational cost occupied by most approaches cannot be ignored. Additionally, the global and local modeling of image features is not well utilized and explored, which is important for texture content reconstruction. In this study, we present an efficient and lightweight network based on CNN and transformer for image SR, dubbed dual-aware transformer network (DATN). Specifically, a dual-aware fusion module (DAFM), consisting of spatial-aware adaptive block (SAAB) and global-aware fusion block (GAFB), learns local and global features in a complementary manner for yielding abundant content details. SAAB delivers the captured spatial information to GAFB in parallel, whereas GAFB establishes long-range spatial dependencies among diverse features to boost the network's ability for recovering texture details. Further, in the upsampling stage, our proposed transformer-empowered upsampling module aggregates global information from the transformer module to generate location-wise reassembly kernels for content-aware upsampling. In such way, feature-rich information is highlighted adaptively to reconstruct high-quality images with maximum efficiency. Experimental results reveal that our proposed DATN achieves high qualitative performance while maintaining low computational demands, surpassing most state-of-the-art SR networks.

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Step by step: A hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning

Cited by 26 publications

References 27 publications

Knowledge Graphs: Opportunities and Challenges

Knowledge Graphs: Opportunities and Challenges

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Dual-aware transformer network for single-image super-resolution

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