5 papers
Enhancing Knowledge Graph Completion with GNN Distillation and Probabilistic Interaction Modeling
Lingzhi Wang, Pengcheng Huang, Haotian Li +6
Knowledge graphs (KGs) serve as fundamental structures for organizing interconnected data across diverse domains. However, most KGs remain incomplete, limiting their effectiveness…
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…
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…
DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning
Haotian Li, Hongri Liu, Yao Wang +2
Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human pr…
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.…