77 citations · 144 across the 11 of their papers we have counts for
7 papers · 1 filter
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can gras…
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
Xin Lv, Xu Han, Lei Hou +6
Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are d…
Neural Abstractive Summarization with Structural Attention
Tanya Chowdhury, Sachin Kumar, Tanmoy Chakraborty
Attentional, RNN-based encoder-decoder architectures have achieved impressive performance on abstractive summarization of news articles. However, these methods fail to account for…
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…