Federated knowledge graph completion via embedding-contrastive learning

Chen, Mingyang; Zhang, Wen; Yuan, Zonggang; Jia, Yunyi; Chen, Huajun

doi:10.1016/j.knosys.2022.109459

Cited by 21 publications

(15 citation statements)

References 11 publications

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“…Currently, there are only [47] and [48] work on federal graph contrastive learning, but they focuses on data heterogeneity and reducing structural interference to graph federated learning due to differential privacy via graph contrastive learning methods. Our work focuses on addressing the limitations of distributed storage for GCL through federated learning.…”

Section: B Federated Learningmentioning

confidence: 99%

SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning

Wang

Zheng

Gao

et al. 2023

IEEE Open J. Comput. Soc.

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As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, etc. One prerequisite for graph contrastive learning is the support of huge graphs in the training procedure. However, the graph data nowadays are distributed in various devices and hold by different owners, like those smart devices in Internet of Things. Considering the non-negligible consumptions on computing, storage, communication, data privacy and other issues, these devices often prefer to keep data locally, which significantly reduces the graph contrastive learning performance. In this paper, we propose a novel federal graph contrastive learning framework. First, it is able to update node embeddings during training by means of a federation method, allowing the local GCL to acquire anchors with richer information. Second, we design a Self-adaptive Cluster-based server strategy to select the optimal embedding update scheme, which maximizes the richness of the embedding information while avoiding the interference of noise. Generally, our method can build anchors with richer information through a federated learning approach, thus alleviating the performance degradation of graph contrastive learning due to distributed storage. Extensive analysis and experimental results demonstrate the superiority of our framework.

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Section: B Federated Learningmentioning

confidence: 99%

SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning

Wang

Zheng

Gao

et al. 2023

IEEE Open J. Comput. Soc.

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“…Datasets This study follows the experimental setups of DacKGR (Lv et al, 2020) and HoGRN (Chen et al, 2022) on two sparse datasets, namely WD-singer and NELL23K.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…1, the performance curves exhibit a clear downward trend as sparsity increases. Unfortunately, KGs in practical applications are typically much sparser than those in current research (Chen et al, 2022), considering the insufficient corpus and imperfect effect for the information extraction technology (Xu et al, 2023;Sui et al, 2023). Therefore, investigating sparse KGs would greatly benefit real-world applications such as question answering (Cao et al, 2022;Galkin et al, 2022), conversation (Li et al, 2022a), and question generation (Fei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…These two components are jointly optimized using a well-designed variational EM algorithm. Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.Previous studies have utilized the graph attention mechanism within the Graph Neural Network (GNN) framework (Chen et al, 2022) or contrastive learning (Tan et al, 2023) to address the issue of graph sparsity. However, these methods only consider the first-order neighbor structure and disregard rich higher-order graph structure features such as rules and motifs (Jin et al, 2022), which are essential semantic components of the graph structure (Yang et al, 2018).…”

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confidence: 99%

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Hyperplane-based time-aware knowledge graph embedding for temporal knowledge graph completion

Zhou²,

Liu³

et al. 2022

IFS

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Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.

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“…Knowledge graph is essentially a large-scale semantic network, which is rich in concepts, entities and semantic relationships [ 21 , 22 ]. As a semantic network, knowledge atlas is an important method of knowledge representation in the age of big data.…”

Section: Introductionmentioning

confidence: 99%

Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis

Gao

Yang

Xue

et al. 2023

Life

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Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and research hotspot of the relationship between cancer and obesity by using document statistics and knowledge graph visualization technology. Through the visualization technology analysis of knowledge graph in this study, the research hotspot and knowledge base source of the relationship between cancer and obesity in the last 20 years have been ascertained. Obesity-related factors, such as immunity, insulin, adiponectin, adipocytokines, nonalcoholic fatty liver and inflammatory reaction, may affect the occurrence of obesity and increase the risk of cancer. Obesity-related cancers include respiratory cancer, colorectal cancer, hepatocellular cancer, prostate cancer, gastric cancer, etc. Our research provides direction and basis for future research in this field, as well as technical and knowledge basis support for experts and researchers in related medical fields.

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Federated knowledge graph completion via embedding-contrastive learning

Cited by 21 publications

References 11 publications

SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning

SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning

Hyperplane-based time-aware knowledge graph embedding for temporal knowledge graph completion

Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis

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