370 citations · 1.5k across the 61 of their papers we have counts for
14 papers · 1 filter
Reasoning with Language Model Prompting: A Survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang +6
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This…
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
Xin Xu, Xiang Chen, Ningyu Zhang +3
This paper presents an empirical study to build relation extraction systems in low-resource settings. Based upon recent pre-trained language models, we comprehensively investigate…
Multimodal Analogical Reasoning over Knowledge Graphs
Ningyu Zhang, Lei Li, Xiang Chen +3
Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning a…
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Xiang Chen, Ningyu Zhang, Lei Li +6
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction. However, existing approaches for MNER and…
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Xiang Chen, Ningyu Zhang, Lei Li +6
Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering…
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Xiang Chen, Lei Li, Ningyu Zhang +4
Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relati…
Contrastive Demonstration Tuning for Pre-trained Language Models
Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng +3
Pretrained language models can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searchin…
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
Wen Zhang, Xiangnan Chen, Zhen Yao +10
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) met…
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Xin Xie, Ningyu Zhang, Zhoubo Li +5
Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completi…
Ontology-enhanced Prompt-tuning for Few-shot Learning
Hongbin Ye, Ningyu Zhang, Shumin Deng +5
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to ben…
OntoProtein: Protein Pretraining With Gene Ontology Embedding
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang +6
Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language…
Kformer: Knowledge Injection in Transformer Feed-Forward Layers
Yunzhi Yao, Shaohan Huang, Li Dong +3
Recent days have witnessed a diverse set of knowledge injection models for pre-trained language models (PTMs); however, most previous studies neglect the PTMs' own ability with qua…
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Peng Wang, Xin Xie, Xiaohan Wang +1
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to rea…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…