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Composition-based Multi-Relational Graph Convolutional Networks
TL;DR: This paper proposes CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph and leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations.
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Abstract: Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it. Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only. In this paper, we propose CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph. CompGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations. It also generalizes several of the existing multi-relational GCN methods. We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results. We make the source code of CompGCN available to foster reproducible research.
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A Survey on Knowledge Graphs: Representation, Acquisition and Applications
TL;DR: A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.
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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Jiapeng Wu,Meng Cao,Jackie Chi Kit Cheung,William L. Hamilton +3 more
- 01 Nov 2020
TL;DR: The Temporal Message Passing (TeMP) framework is proposed, which combines graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques to address the temporal sparsity and variability of entity distributions in TKGs.
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings
TL;DR: This paper proposes HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood and designs a masked entity prediction task to balance information from the relational context and the source entity itself.
Neural, symbolic and neural-symbolic reasoning on knowledge graphs
Jing Zhang,Bo Chen,Lingxi Zhang,Xirui Ke,Haipeng Ding +4 more
- 01 Jan 2021
TL;DR: This survey takes a thorough look at the development of the symbolic, neural and hybrid reasoning on knowledge graphs, and surveys two specific reasoning tasks — knowledge graph completion and question answering on knowledgeGraphs, and explains them in a unified reasoning framework.
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Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
TL;DR: This work introduces a realistic problem of few-shot out-of-graph link prediction, where the links between the seen and unseen nodes as in a conventional out- of-knowledge link prediction but also between the unseen nodes, with only few edges per node.
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