5 papers
Fine-Grained Table Retrieval Through the Lens of Complex Queries
Wojciech Kosiuk, Xingyu Ji, Yeounoh Chung +2
Enabling question answering over tables and databases in natural language has become a key capability in the democratization of insights from tabular data sources. These systems fi…
TARGET: Benchmarking Table Retrieval for Generative Tasks
Xingyu Ji, Parker Glenn, Aditya G. Parameswaran +1
The data landscape is rich with structured data, often of high value to organizations, driving important applications in data analysis and machine learning. Recent progress in repr…
Self-Explainable Graph Transformer for Link Sign Prediction
Lu Li, Jiale Liu, Xingyu Ji +2
Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN…
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Yiru Pan, Xingyu Ji, Jiaqi You +5
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulatio…
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
Xingyu Ji, Jiale Liu, Lu Li +2
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Netwo…