2 citations · 2 across the 3 of their papers we have counts for
4 papers
Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering
Yongxue Shan, Meihan Wu, Cundi Fang +2
Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centere…
LeAdQA: LLM-Driven Context-Aware Temporal Grounding for Video Question Answering
Xinxin Dong, Baoyun Peng, Haokai Ma +4
Video Question Answering (VideoQA) requires identifying sparse critical moments in long videos and reasoning about their causal relationships to answer semantically complex questio…
EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs
Zixuan Dong, Baoyun Peng, Yufei Wang +4
While large language models (LLMs) have shown remarkable capabilities in natural language processing, they struggle with complex, multi-step reasoning tasks involving knowledge gra…
Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion
Yongxue Shan, Jie Zhou, Jie Peng +3
In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representa…