77 citations · 144 across the 11 of their papers we have counts for
12 papers
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application
Wen Zhang, Chi-Man Wong, Ganqinag Ye +4
In recent years, knowledge graphs have been widely applied as a uniform way to organize data and have enhanced many tasks requiring knowledge. In online shopping platform Taobao, w…
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
Wen Zhang, Chi-Man Wong, Ganqiang Ye +3
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has g…
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…
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
Yushan Zhu, Wen Zhang, Mingyang Chen +4
Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. Howe…
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…
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 "…