4 citations · 10 across the 4 of their papers we have counts for
8 papers
Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
Guangyi Liu, Yongqi Zhang, Xunyuan Liu +1
Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI predict…
GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs
Enjun Du, Siyi Liu, Yongqi Zhang
Knowledge graph reasoning in the fully-inductive setting, where both entities and relations at test time are unseen during training, remains an open challenge. In this work, we int…
Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes
Zhenqian Shen, Mingyang Zhou, Yongqi Zhang +1
Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Cu…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
Shimin Di, Quanming Yao, Yongqi Zhang +1
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also…
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Wenyuan Dai +1
Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different…