791 citations · 2.5k across the 66 of their papers we have counts for
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Search to Pass Messages for Temporal Knowledge Graph Completion
Zhen Wang, Haotong Du, Quanming Yao +1
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topologica…
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Hansi Yang, Yongqi Zhang, Quanming Yao
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on differe…
Knowledge Graph Reasoning with Relational Digraph
Yongqi Zhang, Quanming Yao
Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning…
Bilinear Scoring Function Search for Knowledge Graph Learning
Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been…
Role-Aware Modeling for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play diffe…
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Yingxia Shao +1
Knowledge Graph (KG) embedding is a fundamental problem in data mining research with many real-world applications. It aims to encode the entities and relations in the graph into lo…
Automated Machine Learning: From Principles to Practices
Zhenqian Shen, Yongqi Zhang, Lanning Wei +2
Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious. To…