31 citations · 131 across the 30 of their papers we have counts for
7 papers · 2 filters
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Yuxia Geng, Jiaoyan Chen, Yuhang Zeng +5
Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they ha…
MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
Yichi Zhang, Zhuo Chen, Wen Zhang
Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity…
CausE: Towards Causal Knowledge Graph Embedding
Yichi Zhang, Wen Zhang
Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the…
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
Yufeng Huang, Jiji Tang, Zhuo Chen +8
Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on i…
Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
Yichi Zhang, Mingyang Chen, Wen Zhang
Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, exi…
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
Mingyang Chen, Wen Zhang, Zhen Yao +4
We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional kno…
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Mingyang Chen, Wen Zhang, Yuxia Geng +3
Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent ye…