77 citations · 144 across the 11 of their papers we have counts for
6 papers · 1 filter
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Zequn Sun, Chengming Wang, Wei Hu +4
Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real…
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang +3
Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and re…
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficu…
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Mingyang Chen, Wen Zhang, Wei Zhang +2
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only h…
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant…
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Ningyu Zhang, Shumin Deng, Zhanlin Sun +4
We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "…