关于我👨🎓
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About ME¶
- I'm Zhang-Each
- Master student in Zhejiang University, major in Computer Science and Technology.
- Study Knowledge Graphs and NLP in ZJU-KG lab.
- Blog: link here
- Notebook: link here
- Google Scholar: link here
Skill & Interest¶
- Languages：C/C++, Java, Python, Go, Rust, LaTeX, Markdown, PPT
- Frameworks: PyTorch, DGL, React, SpringBoot
- Tools: Linux, VSCode, PyCharm, Obsidian
- Interest areas: Knowledge Graphs, Natural Language Processing, Multi-modal Machine Learning, Machine Learning Systems
Preprint¶
- Making Large Language Models Perform Better in Knowledge Graph Completion. (Preprint paper, ArXiv)
- Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering. (Preprint paper. ArXiv)
- Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey. ArXiv
Publication¶
- Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling. (First Author, KDD-2022 Undergraduate consortium, paper)
- Tele-Knowledge Pre-training for Fault Analysis. (ICDE-2023 Industry Track, paper)
- Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding. (Accepted by IJCNN 2023, paper)
- CausE: Towards Causal Knowledge Graph Embedding. (Accepted by CCKS 2023, paper)
- MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion. (Accepted by NLPCC 2023, paper)
- Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion . (Accepted by COLING 2024 paper)
Projects¶
- NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs. Github
Internship¶
- TBD
Stats¶