3 citations · 5 across the 5 of their papers we have counts for
5 papers
Deep Sparse Latent Feature Models for Knowledge Graph Completion
Haotian Li, Rui Zhang, Lingzhi Wang +6
Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these m…
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
Aiheng Zhang, Qiguang Jiang, Kai Wang +1
With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area n…
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
Kai Wang, Qiguang Jiang, Bailing Wang +2
In-vehicle network (IVN) is facing complex external cyber-attacks, especially the emerging masquerade attacks with extremely high difficulty of detection while serious damaging eff…
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation
Haotian Li, Bin Yu, Yuliang Wei +3
Knowledge graph completion (KGC) revolves around populating missing triples in a knowledge graph using available information. Text-based methods, which depend on textual descriptio…
An original model for multi-target learning of logical rules for knowledge graph reasoning
Yuliang Wei, Haotian Li, Guodong Xin +2
Large-scale knowledge graphs provide structured representations of human knowledge. However, as it is impossible to collect all knowledge, knowledge graphs are usually incomplete.…