LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

Ning, Zhiyuan; Qiao, Ziyue; Dong, Hao; Du, Yi; Zhou, Yuanchun

doi:10.1007/978-3-030-75768-7_15

Cited by 5 publications

(2 citation statements)

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“…By taking unique local structures like cycles and stars into account, a novel graph attention network named LSA-GAT [26] derives a sophisticated representation covering both the semantic and structural information. Lightweight Framework for Context-Aware Knowledge Graph Embedding (LightCAKE) [37] focuses on graph context. The novel aspect of this technique is the construction of a context star network to model the entity/relation context.…”

Section: Figure 3 the Zero-shot Learning Modelsmentioning

confidence: 99%

A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs

Kumar

Sing

et al. 2022

IEEE Access

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The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the existing knowledge graph still has some problems which form hot research topics in recent years. Numerous methods have been proposed based on various representation techniques. Graph Neural Network, a framework that uses deep learning to process graph-structured data directly, has significantly advanced the state-of-the-art in the past few years. This study firstly is aimed at providing a broad, complete as well as comprehensive overview of GNN-based technologies for solving four different KG tasks, including link prediction, knowledge graph alignment, knowledge graph reasoning, and node classification. Further, we also investigated the related artificial intelligence applications of knowledge graphs based on advanced GNN methods, such as recommender systems, question answering, and drugdrug interaction. This review will provide new insights for further study of KG and GNN.

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Section: Figure 3 the Zero-shot Learning Modelsmentioning

confidence: 99%

A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs

Kumar

Sing

et al. 2022

IEEE Access

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“…Their recurrent transformer enables to transform global KGEs into contextual embeddings, given the situation-specific factors of the relation and the subjective history of the entity. Ning et al [37] proposed a lightweight framework for the usage of context within standard embedding methods. Wang et al [47] presented a deep contextualized knowledge graph embedding method that learns representations of entities and relations from constructed contextual entity-relation chains.…”

Section: Related Workmentioning

confidence: 99%

Context-Driven Visual Object Recognition Based on Knowledge Graphs

Monka

Halilaj

Rettinger

2022

The Semantic Web – ISWC 2022

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Current deep learning methods for object recognition are purely data-driven and require a large number of training samples to achieve good results. Due to their sole dependence on image data, these methods tend to fail when confronted with new environments where even small deviations occur. Human perception, however, has proven to be significantly more robust to such distribution shifts. It is assumed that their ability to deal with unknown scenarios is based on extensive incorporation of contextual knowledge. Context can be based either on object co-occurrences in a scene or on memory of experience. In accordance with the human visual cortex which uses context to form different object representations for a seen image, we propose an approach that enhances deep learning methods by using external contextual knowledge encoded in a knowledge graph. Therefore, we extract different contextual views from a generic knowledge graph, transform the views into vector space and infuse it into a DNN. We conduct a series of experiments to investigate the impact of different contextual views on the learned object representations for the same image dataset. The experimental results provide evidence that the contextual views influence the image representations in the DNN differently and therefore lead to different predictions for the same images. We also show that context helps to strengthen the robustness of object recognition models for out-of-distribution images, usually occurring in transfer learning tasks or real-world scenarios.

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