Learning graph attention-aware knowledge graph embedding

Li, Chen; Peng, Xutan; Niu, Yuhang; Zhang, Shanghang; Peng, Hao; Zhou, Chuan; Li, Jianxin

doi:10.1016/j.neucom.2021.01.139

Cited by 16 publications

(3 citation statements)

References 43 publications

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“…The initial knowledge graph is usually symbolic text description data, and we need to vectorize it so that the computer can use it for various tasks [38]- [40]. Knowledge embedded learning is trying to learn a projection from symbolic space to low-dimensional vectors, such as TransE and its variants [41]- [44].…”

Section: A Knowledge Embedded Learningmentioning

confidence: 99%

“…Baselines and Implementation Details We compare the proposed algorithm with the following methods: TransE 1 [8], TransH 1 [10], TransR 1 [11], TransD 1 [12], DKRL [21], TKRL [22], ConvE 2 , GAKE [38], CTransR 3 , PTransE [43], pTransE [52], TEKE [27], EDGE [53], TransP [58], AMC-NN [59], CRAN [60] and GAATs [61]. For TransE, TransH, TransD and TransR, we learn a separate embedding matrix using the positive training entity pairs.…”

Section: A Datasets and Experimental Setupmentioning

confidence: 99%

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A Knowledge Graph Embedding Framework With Triple Semantics

et al. 2023

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The knowledge graph embedding model aims to use low-dimensional real-valued vectors to represent the entities and relations in the triples, where operations such as link prediction and triple classification can be performed based on these representations. However, existing embedding models only consider the structural embedding of triples, while ignoring the semantic information of triples. This paper proposes a knowledge graph embedding learning framework combined with triple semantic information (KGSE). KGSE comprehensively considers the structural embedding and semantic embedding of triples, where semantic embedding is used as a supplement to improve the quality of embedding. Specifically, KGSE uses the improved TransD model to obtain the structural embedding of triples, and employs the deep convolutional neural model combined with an attention mechanism to obtain the semantic embedding of triples. In addition, a novel energy function is designed to jointly train the above two embeddings. Experimental results show that the proposed framework improves significantly compared with Trans-based models and other baseline models in link prediction and triple classification tasks, which verifies the effectiveness of the proposed framework.

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Section: A Knowledge Embedded Learningmentioning

confidence: 99%

Section: A Datasets and Experimental Setupmentioning

confidence: 99%

A Knowledge Graph Embedding Framework With Triple Semantics

et al. 2023

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“…GraphSAGE minimizes information loss by concatenating vectors of neighbors rather than summing them into a single value in the process of neighbor aggregation [40,41]. GAT utilizes the concept of attention to individually deal with the importance of neighbor nodes or relations [21,[42][43][44][45][46][47]. Since each model has different characteristics and advantages, suitable models for KG alignment differ depending on the components and the topological structure of the KG.…”

Section: Knowledge Graph Alignmentmentioning

confidence: 99%

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Kim

Sohn

2022

Sensors

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Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber–physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.

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A transformer framework for generating context-aware knowledge graph paths

Lim

2023

Appl Intell

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Learning graph attention-aware knowledge graph embedding

Cited by 16 publications

References 43 publications

A Knowledge Graph Embedding Framework With Triple Semantics

A Knowledge Graph Embedding Framework With Triple Semantics

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

A transformer framework for generating context-aware knowledge graph paths

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