About ME

• I'm Zhang-Each
  • A graduate student in Zhejiang University, major in Computer Science
  • Study Knowledge Graphs(KG) and Natural Language Processing(NLP) in ZJU-KG lab.
• Learning open courses released by Stanford/MIT/CMU
• Blog: link here
• Notebook: link here
• Personal Page: link here

Publication

• Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling. (First Author, Accepted by KDD-2022 Undergraduate consortium, ArXiv)
• Tele-Knowledge Pre-training for Fault Analysis. (Accepted by ICDE-2023 Industry Track, ArXiv)
• Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding. (Accepted by IJCNN 2023, ArXiv)
• CausE: Towards Causal Knowledge Graph Embedding. (Accepted by CCKS 2023, ArXiv)
• MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion. (Accepted by NLPCC 2023, ArXiv)
• Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion. (Accepted by COLING 2024, ArXiV)
• NativE: Multi-modal Knowledge Graph Completion in the Wild. (Accepted by SIGIR 2024, ArXiV).
• Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering. (Accepted by ACL 2024 Findings, ArXiv)

Preprint

• Making Large Language Models Perform Better in Knowledge Graph Completion. (ArXiv)
• Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey. (ArXiv)
• MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion. (ArXiv)
• Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks. (ArXiV)
• Mixture of Modality Knowledge Experts for Robust Multi-modal Knowledge Graph Completion. (ArXiV)

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