A5042467338- Advanced Graph Neural Networks
- Topic Modeling
- Natural Language Processing Techniques
- Bayesian Modeling and Causal Inference
- Cloud Data Security Solutions
- Access Control and Trust
Wuhan University
2022-2024
Complex knowledge graph question answering (KGQA) aims at natural language questions by entities retrieving from a (KG). Recently, the relation path-based models have shown unique advantage for complex KGQA. However, these existing ignore dependency between different paths, which leads to aimless reasoning over KG. To resolve this issue, we propose question-directed with relation-aware attention network (QRGAT) that encodes process as graph. The GAT can recognize neighbor along corresponding...
Multihop question answering from knowledge bases (KBQA) is a hot research topic in natural language processing. Recently, the graph neural network-based (GNN-based) methods have achieved promising results as KB can be organized (KG). However, they often suffered sparsity of KG which was detrimental to structure encoding and reasoning capabilities GNN. Specifically, sparse linked by directed relations previous studies paid scant attention directional characteristic KG, limiting patterns...