arXiv：cs.LG· Runze Li, Hanchen Wang, Ying Zhang, Wenjie Zhang·· 4 小时前AI 评分36
JevNexus：以决策为中心的 schema 匹配框架
Correspondences as Decisions: JevNexus for Decision-Centric Schema Matching
AI 导读
JevNexus 将 schema 匹配建模为有界对应决策，结合类型化成对决策与 schema/实例证据，仅在证据不一致且融合间隔较小时才触发 listwise 精排。
正文
Abstract:Schema matching increasingly uses generative language models to rerank retrieved column candidates, although the underlying task is a bounded correspondence decision. We present JevNexus, which combines typed pairwise decisions with schema/instance evidence and invokes listwise refinement only when the evidence disagrees and the fused margin is small. The evaluation covers 561 cases from six benchmark families. JevNexus obtains dataset-macro MRR and Hits@1 of 0.930 and 0.909, compared with 0.926 and 0.903 for Magneto, while reducing mean latency from 123.452 to 15.929 seconds (7.750). Paired analysis finds no statistically significant difference in either MRR or Hits@1. The gate invokes listwise refinement for only 5.665% of source columns and avoids the degradation caused by unconditional refinement. Code and experimental artifacts are available at this https URL.
| Comments: | 12 pages, 9 figures |
| Subjects: | Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09487 [cs.DB] |
| (or arXiv:2610.09487v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09487 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Runze Li [view email]
[v1]
Wed, 7 Oct 2026 05:42:41 UTC (630 KB)
来源：arXiv：cs.LG · arxiv.org