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 https://github.com/RazeenLI/JevNexus.
Figures & tables
Figure 1. A schema-matching example with conflicting evidence.
Figure 2. Generative and decision-centric inference over the same localized candidates.
Figure 3. Overview of the JevNexus matching pipeline.
Figure 4. Typed correspondence decisions for two localized candidates.
Dataset
Cases
Source cols.
Source width
Target width
GT pairs
GT/case
ChEMBL
180
3,060
12–23
12–23
2,052
11.4
GDC
10
569
16–179
736
259
25.9
Magellan
7
41
4–9
4–9
41
5.9
OpenData
180
6,876
26–51
26–51
4,572
25.4
TPC-DI
180
2,916
11–22
12–22
1,980
11.0
WikiData
4
60
13–20
13–20
40
10.0
Table 1. Dataset statistics.
Figure 5. Overall matching effectiveness.
ChEMBL
GDC
Magellan
OpenData
TPC-DI
WikiData
Method
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
COMA
0.790
0.715
0.599
0.521
0.481
0.314
1.000
1.000
1.000
0.814
0.781
0.641
0.903
0.881
0.876
0.784
0.748
0.665
COMA++
0.896
0.847
0.747
0.561
0.507
0.366
1.000
1.000
1.000
0.911
0.891
0.514
0.972
0.963
0.870
0.946
0.946
0.873
Distribution
0.549
0.438
0.451
0.000
0.000
0.000
0.686
0.561
0.561
0.563
0.482
0.282
0.790
0.722
0.584
0.808
0.773
0.644
Similarity Flooding
0.793
0.659
0.447
0.544
0.467
0.280
1.000
1.000
1.000
0.650
0.554
0.420
0.877
0.825
0.665
0.766
0.710
0.646
ISResMat
0.838
0.782
0.733
0.448
0.379
0.258
1.000
1.000
1.000
0.686
0.587
0.514
0.868
0.794
0.767
0.946
0.946
0.873
Table 2. Per-dataset matching effectiveness.
Figure 6. Matching effectiveness and inference efficiency.
ChEMBL
GDC
Magellan
OpenData
TPC-DI
WikiData
Variant
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
MRR
H@1
R@GT
w/o Both
0.830
0.766
0.616
0.739
0.642
0.376
0.944
0.895
0.855
0.918
0.882
0.620
0.961
0.931
0.774
0.868
0.817
0.773
w/o Complement
0.869
0.832
0.638
0.797
0.751
0.400
0.990
0.980
0.911
0.948
0.935
0.626
0.941
0.903
0.762
0.889
0.858
0.773
w/o Refinement
0.904
0.873
0.783
0.781
0.703
0.456
1.000
1.000
1.000
0.950
0.935
0.647
0.980
0.963
0.865
0.946
0.946
0.902
Always Refine
0.901
0.865
0.762
0.798
0.730
0.454
1.000
1.000
1.000
0.952
0.939
0.645
0.935
0.889
0.829
0.924
0.902
0.902
JevNexus
0.907
0.878
0.783
0.794
0.725
0.456
1.000
1.000
1.000
0.952
0.939
0.648
0.980
0.964
0.865
0.946
0.946
0.902
Table 3. Component ablation by dataset.
Dataset
Sources
Disagree
Low margin
Calls
Avoided
Activation (%)
ChEMBL
3,060
711
717
111
2,949
3.627
GDC
569
179
189
38
531
6.678
Magellan
41
4
0
0
41
0.000
OpenData
6,876
1,596
1,515
521
6,355
7.577
TPC-DI
2,916
338
786
96
2,820
3.292
WikiData
60
9
7
0
60
0.000
Table 4. Observed online routing by dataset.
Figure 7. Effects of evidence integration and selective refinement.
Figure 8. Hyperparameter sensitivity on the development subset.
Figure 9. Candidate coverage and stage-wise error decomposition.