370 citations · 1.8k across the 232 of their papers we have counts for
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MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Zhuo Chen, Jiaoyan Chen, Wen Zhang +8
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current…
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…
On Analyzing the Role of Image for Visual-enhanced Relation Extraction
Lei Li, Xiang Chen, Shuofei Qiao +3
Multimodal relation extraction is an essential task for knowledge graph construction. In this paper, we take an in-depth empirical analysis that indicates the inaccurate informatio…
Generative Knowledge Graph Construction: A Review
Hongbin Ye, Ningyu Zhang, Hui Chen +1
Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be a…
Tele-Knowledge Pre-training for Fault Analysis
Zhuo Chen, Wen Zhang, Yufeng Huang +14
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge no…
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
Yunzhi Yao, Shengyu Mao, Ningyu Zhang +4
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performa…
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…
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan +4
In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical cha…
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…
Neural-Symbolic Entangled Framework for Complex Query Answering
Zezhong Xu, Wen Zhang, Peng Ye +2
Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query…
Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graph
Zezhong Xu, Peng Ye, Hui Chen +3
Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing works mainly concentrate on mining rules. However, there might be several rules that could be…
Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
Shumin Deng, Chengming Wang, Zhoubo Li +11
Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. De…
Multi-modal Protein Knowledge Graph Construction and Applications
Siyuan Cheng, Xiaozhuan Liang, Zhen Bi +2
Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate r…
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-s…
Disentangled Ontology Embedding for Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Wen Zhang +6
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). Ho…
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
Mingyang Chen, Wen Zhang, Zhen Yao +4
We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this…
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…
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Xiang Chen, Lei Li, Ningyu Zhang +6
Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the…
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
Zhen Bi, Siyuan Cheng, Jing Chen +3
Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer archit…
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
Yincen Qu, Ningyu Zhang, Hui Chen +6
In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for `…
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…
Deep Reinforcement Learning for Entity Alignment
Lingbing Guo, Yuqiang Han, Qiang Zhang +1
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies. Although great promise they can offer, there are still several limitations. The…