Towards Multi-Table Learning: A Novel Paradigm for Complementarity Quantification and Integration
Junyu Zhang, Lizhong Ding, MinghongZhang, Ye Yuan, Xingcan Li, Pengqi Li, Tihang Xi, Guoren Wang, Changsheng Li
Multi-table data integrate various entities and attributes, with potential interconnections between them. However, existing tabular learning methods often struggle to describe and leverage the underlying complementarity across distinct tables. To address this limitation, we propose a novel paradigm for multi-table learning that, for the first time, systematically quantifies and integrates complementary information across multiple tables. Specifically, we introduce a metric called complementarity strength (CS), which captures inter-table complementarity by incorporating relevance, similarity, and informativeness. For the first time, we systematically formulate the paradigm towards multi-table learning by establishing formal definitions of tasks and loss functions. Correspondingly, we present the network towards multi-table learning that combines Adaptive Table encoder and Cross table Attention mechanism (ATCA-Net), achieving the simultaneous integration of complementary information from distinct tables. Extensive experiments show that ATCA-Net effectively leverages complementary information, while demonstrating that the CS metric accurately quantifies complementarity richness across multiple tables. To the best of our knowledge, this is the first work to establish theoretical and practical foundations for multi-table learning.