3 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Unlearning of Knowledge Graph Embedding via Preference Optimization
Jiajun Liu, Wenjun Ke, Peng Wang +5
Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge,…
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
Jiajun Liu, Wenjun Ke, Peng Wang +7
Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating cat…
Towards Continual Knowledge Graph Embedding via Incremental Distillation
Jiajun Liu, Wenjun Ke, Peng Wang +5
Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To addr…
Revisiting Large Language Models as Zero-shot Relation Extractors
Guozheng Li, Peng Wang, Wenjun Ke
Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data even if under zero-shot setting. Recent studies have shown that large language models (…