Yongquan Ji — accepted AI-conference papers

2025

Breaking the Noise Barrier: LLM-Guided Semantic Filtering and Enhancement for Multi-Modal Entity Alignment

Chenglong Lu, Chenxiao Li, Jingwei Cheng, Yongquan Ji, Guoqing Chen, Fu Zhang

EMNLP 2025

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs (MMKGs). However, the intrinsic noise within modalities, such as the inconsistency in visual modality and redundant attributes, has not been thoroughly investigated. Excessive noise not o

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2025

Capturing Latent Modal Association For Multimodal Entity Alignment

Yongquan Ji, Jingwei Cheng, Fu Zhang, Chenglong Lu

EMNLP 2025

Multimodal entity alignment aims to identify equivalent entities in heterogeneous knowledge graphs by leveraging complementary information from multiple modalities. However, existing methods often overlook the quality of input modality embeddings during modality interaction – such as missing modalit

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