LLMATCH: A Unified Schema Matching Framework with Large Language Models

Bing Tian Dai; Hanhua Xiao; Lambert Deng; Roy Ka-Wei Lee; Sha Wang; Yanfei Dong; Yuchen Li

REVIEW 4 major objections 7 minor 43 references

LLMATCH: A Unified Schema Matching Framework with Large Language Models

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read LLMatch, a staged LLM framework with Rollup and Drilldown, improves accuracy on complex multi-table schema matching and accelerates real-world data integration.

desk verdict A modular LLM schema matching framework with a useful Rollup/Drilldown trick and a new multi-table benchmark; the core claim holds up, but the evidence needs error bars and a closer look at the benchmark ground truth. read the letter →

arxiv 2507.10897 v1 pith:V27WPBAM submitted 2025-07-15 cs.DB

Sha Wang , Yuchen Li , Hanhua Xiao , Bing Tian Dai , Roy Ka-Wei Lee , Yanfei Dong , Lambert Deng This is my paper · ORCID

classification cs.DB

keywords schemamatchinglargelanguagemodelsdataintegrationmulti-tablealignmentNetRollupandDrilldownOMOP

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Schema matching is the task of aligning columns across databases, and until recently automatic tools handled only simple single-table cases. The paper tries to establish that complex multi-table matching becomes accurate when a large language model is used inside a deliberately staged pipeline rather than in one monolithic prompt. The pipeline first prepares and simplifies both schemas, then selects candidate tables, then aligns columns; the added Rollup step merges semantically related columns into higher-level concepts and Drilldown re-expands the matched concepts to decide fine-grained column correspondences. The authors report that on their new SchemaNet benchmark of seven real-world schema pairs, this design outperforms all baselines on complex tasks, and on the largest task it reaches F1 0.4, double the 0.2 of the strongest prior LLM baseline, while staying competitive on simple tasks. A small user study is also used to argue that machine assistance improves annotator matching accuracy.

What carries the argument

The Rollup and Drilldown pair is the load-bearing mechanism. Rollup is an LLM-driven transformation that merges semantically related columns, for example time fields into a timestamp alias, to shrink the matching problem; Drilldown is the inverse refinement that, for any coarse match involving an alias, re-runs a focused LLM prompt on the original columns and decides which are true correspondences. The surrounding three-stage pipeline, consisting of schema preparation, table-candidate selection, and column-level alignment, makes these transformations composable and lets each component be ablated and evaluated separately.

What would settle it

Independently re-annotate the schema-pair ground truth for the OMOP tasks in SchemaNet, for example mimic_iii-omop and cms-omop, by two domain experts, then recompute F1 for LLMatch and ReMatch; if the corrected mappings shrink or reverse the reported 0.4-versus-0.2 advantage, the central accuracy claim fails.

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Extended reading notes

Core claim

On its own terms, the central discovery is that the hard part of complex schema matching is not the semantic matching decision but the schema's scale and interleaving, and that a hierarchy of abstraction fixes it. Rollup compresses many related columns into a few aliases, allowing coarse matches to be found reliably; Drilldown then revisits only those aliases and asks the LLM to resolve the original columns, deliberately allowing some rolled-up candidates to be rejected. The paper claims this two-level procedure is what lets LLMatch beat all baselines on every complex SchemaNet dataset and match or exceed them on simple datasets. It also reports component-level findings, such as that LLM-based table selection outperforms embedding-based vector similarity, and that descriptions plus primary/foreign-key relationships add the most accuracy.

Load-bearing premise

The accuracy claims stand or fall on SchemaNet's ground-truth mappings being accurate and complete, because every complex-task F1 score is computed against them.

Editorial extensions

If this is right

On complex multi-table benchmarks, a staged LLM pipeline can outperform classical matchers and a strong LLM baseline by large margins, including a doubling of F1 on the largest task.
Because LLM-based table selection beat vector similarity in the authors' experiments, LLM applications that rely on embedding retrieval for candidate selection may have room to improve by using generative selection instead.
The Rollup and Drilldown gains grow with schema complexity, so the framework is best suited to fragmented, richly related enterprise schemas rather than flat single tables.
SchemaNet gives the community a public, multi-domain, multi-table benchmark against which future schema matching work can be measured.
Assisted annotators reached higher F1 in equal time, indicating the framework can reduce manual data-integration effort in practice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct extension the authors do not pursue is wrapping existing matchers with the same Rollup and Drilldown pre- and post-processing to see whether the accuracy gains transfer without retraining the matcher itself.
The recall-prioritized n:m mapping definition implies the method is tuned for data-migration use cases; deployments that need high precision would likely need a ranking or thresholding layer on top of the framework.
The benchmark's ground truth was assembled from prior papers, ETL code, and expert annotation; an independent re-annotation of SchemaNet, especially the OMOP tasks, would test how much of the reported margin is benchmark-specific.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is worth reading. It delivers a clean, modular LLM schema matching framework with a genuinely useful abstraction-refinement trick (Rollup/Drilldown) and a new multi-table benchmark, SchemaNet. The main empirical claim—that this beats existing LLM and traditional matchers on complex multi-table tasks—is plausible and mostly supported by consistent F1 gains across seven datasets. The three-stage decomposition (preparation, table selection, column matching) is a sensible organizational contribution, and the ablations showing component-level contributions are more than most papers in this area bother to do. Releasing the code and dataset is also a real plus.

The soft spots are real but not fatal. First, all F1 numbers come from single runs, and the paper itself acknowledges LLM non-determinism. No error bars, no repeated runs, no statistical tests. For a comparison that hinges on differences like 0.33 vs 0.20, that matters. Second, the stress-test concern about benchmark ground truth is legitimate. SchemaNet mixes datasets adapted from prior work, ones reverse-engineered from ETL code, and one manually annotated pair. There is no inter-annotator agreement or independent audit. Since the OMOP pairs share a single target schema, any systematic bias in annotation granularity could shift F1 for every method and potentially change the ordering. The consistent gains across finance, healthcare, and entertainment are reassuring, but a sensitivity analysis or independent annotation check would make the headline claim much sturdier.

There is also a concrete inconsistency: the text says LLMatch reaches F1 0.4 on mimic_iii-omop, double ReMatch's 0.2, but Table 2 reports 0.33 vs 0.20. That overstatement should be corrected. The productivity study with four participants and two-minute tasks is anecdotal; I would not put much weight on it.

Overall, this is a solid systems paper with a clear architecture and a benchmark the community can use. The central argument holds up qualitatively, and the flaws are fixable with transparency and a bit of extra experimentation. It deserves serious peer review, not a desk reject.

Recommendation: send to review, with the request that the authors add repeated runs or variance estimates, document the benchmark annotation process more carefully, and fix the F1 discrepancy.

Referee Report

4 major / 7 minor

Summary. The paper presents LLMatch, a modular schema matching framework that decomposes the task into schema preparation, table-candidate selection, and column-level alignment. A Rollup module abstracts semantically related columns into higher-level concepts, and a Drilldown module re-expands them for fine-grained matching. The authors also introduce SchemaNet, a benchmark of seven real-world complex schema pairs from finance, healthcare, and entertainment, and report experiments on eleven tasks comparing LLMatch with Coma, SF, Cupid, Unicorn, and ReMatch. The paper claims that LLMatch improves F1 scores on complex matching tasks and increases engineer productivity, with code and benchmark released publicly.

Significance. If the empirical claims hold, the paper makes a useful contribution: LLMatch's modular design enables component-level evaluation, the Rollup/Drilldown idea is a plausible approach for large multi-table schemas, and SchemaNet addresses a real gap by providing complex, multi-table benchmark data. The public release of code, prompts, and benchmark is a concrete strength that should facilitate reproducibility and follow-up work. However, the evidence supporting the central accuracy and productivity claims is currently incomplete: the evaluation lacks variance and significance testing, the benchmark ground truth is not independently validated, and the productivity study is very small. These issues are load-bearing for the paper's main conclusions, so the contribution is promising but not yet fully substantiated.

major comments (4)

[Section 6.2, Tables 2 and 3] All F1 scores are reported as single-run point estimates without variance, confidence intervals, or significance tests. Section 6.2 explicitly acknowledges that LLM outputs are non-deterministic, so the differences supporting the central claim (e.g., Table 2, cms-omop: LLMatch 0.37 vs. ReMatch 0.19; mimic_iii-omop: LLMatch 0.33 vs. ReMatch 0.20) could plausibly fall within run-to-run noise. The paper should report repeated-run means and standard deviations and provide a statistical comparison (e.g., paired tests or effect sizes) at least for the complex tasks.
[Section 5 and Section 6.1] Every complex-task F1 score in Table 2 is computed against SchemaNet's ground-truth mappings, but Section 5 reports no inter-annotator agreement, annotation protocol, or independent audit. The healthcare pairs are reverse-engineered from ETL code and verified by data scientists, and the entertainment pair was manually annotated, but no evidence is given that these mappings are complete and unbiased. Given Section 6.1's n:m mapping definition and the FK-equal-to-PK scoring rule, small annotation decisions (e.g., whether a source column maps to one OMOP column or several conceptual equivalents) change precision and recall for every method and can reverse the LLMatch/ReMatch ordering. The paper should provide a sensitivity analysis against alternative ground-truth definitions or an independent validation of a sample of mappings.
[Section 6.9, Figure 7] The productivity claim rests on four participants, a two-minute time cap, and no protocol description, no inter-subject variance, and no statistical test. Figure 7 reports only average F1 under two conditions, so the abstract's claim of 'substantially boosts engineer productivity' is not supported as stated. A properly designed user study with more participants, task descriptions, and measured time or error rates is needed, or the claim should be appropriately downgraded to a preliminary observation.
[Section 6.7, Figure 6] The Rollup/Drilldown ablation, which is central to the paper's method contribution, does not specify what the 'without Rollup/Drilldown' configuration includes, nor whether table selection and column matching remain identical in both conditions. The reported gains of up to 40% are single-run point estimates. Please provide a precise description of the ablated configuration and variance information so that the contribution of Rollup/Drilldown can be properly assessed.

minor comments (7)

[Section 6.4] The text states that on mimic_iii-omop LLMatch achieves an F1 of 0.4, double ReMatch's 0.2, but Table 2 reports LLMatch 0.33 and ReMatch 0.20. These numbers should be reconciled.
[Figure 4] The y-axis label 'Performance Increase (%)' is not defined; the paper should state the formula used to compute the percentage improvement over ReMatch.
[Section 6.1] The statement that foreign key matches are treated as equal to primary key matches during evaluation is not operationalized; please explain how this rule affects the computation of precision and recall.
[Section 5] For the pairs 'adapted from prior work' (mimic-omop and synthea-omop), the paper does not state what changes were made to the original ground truth or schema; please clarify the adaptation process.
[Table 1] The table header uses footnotes 'a' and 'b' but the notation is confusing; please spell out the column meanings in a clearer way.
[Section 6.2] The model names appear as 'gpt-3.5-turboand gpt-4o-minimodels' (missing spaces) and the temperature/seed settings are not reported; including these details would improve reproducibility.
[Section 6.9] The reference to [42] reporting an 81% labeling cost reduction is not directly measured in this study; the connection should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: LLMatch's outputs are not fitted to its benchmarks, and the only self-citation (the release repository) is not load-bearing.

full rationale

I found no step in which a claimed prediction or derivation reduces by construction to its own input. The method uses LLMs with default settings and no parameter fitting: "We use gpt-3.5-turbo and gpt-4o-mini models with default settings, without any parameter modifications or fine-tuning." The complex-task F1 scores are therefore not fitted to SchemaNet. The Rollup and Drilldown procedure is an algorithmic preprocessing/refinement loop, not an equation derived from the evaluation metric; its effect is measured by ablations on the same data ("with without Rollup/Drilldown"), which is standard comparative evaluation rather than circularity. The only self-reference is the release repository [19], used for code, prompts, and dataset availability ("Both LLMatch and SchemaNet are made public to facilitate further research in schema matching [19]"), and it is not load-bearing for the central accuracy claim. The main residual concern is benchmark validity rather than circularity: SchemaNet is authored by the same team, and Section 5 reports no inter-annotator agreement or independent audit for mappings that were "reverse-engineered from ETL code and verified by data scientists" or "manually annotated by experts." That is a threat to external validity and could change F1 for all methods, but it is not a derivation loop: the gold standard is fixed independently of LLMatch's outputs, and no parameter is learned from it. Similarly, Section 6.2's acknowledgement of "limited reproducibility of specific responses" affects all LLM-based methods equally and does not make any reported result equivalent to its input.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper contributes an engineering framework and benchmark; it does not rest on fitted constants. The main assumptions are domain-level: LLM reliability for semantic grouping and mapping, the correctness of SchemaNet ground truth, and the suitability of the F1 protocol. No new physical or formal entities are introduced.

assumptions (3)

domain assumption LLMs can reliably perform Rollup grouping and column mapping from schema metadata alone.
Used throughout Section 4 and 6; no independent verification beyond the paper's own experiments.
domain assumption SchemaNet ground truth mappings are accurate and complete.
Section 5: some adapted from prior work, some reverse-engineered from ETL code and verified by data scientists; errors would bias all F1 scores.
domain assumption F1 with n:m mapping and FK=PK equivalence is an appropriate evaluation metric.
Section 6.1; this protocol favors recall-oriented methods and may differ from practice where 1:1 precision matters.

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Cite this review

Pith. "Pith review of LLMATCH: A Unified Schema Matching Framework with Large Language Models." pith.science (2026). https://pith.science/paper/V27WPBAM

@misc{pith2026250710897,
  author       = {Pith},
  title        = {Pith review of: LLMATCH: A Unified Schema Matching Framework with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V27WPBAM}},
  note         = {Machine review of arXiv:2507.10897}
}

read the original abstract

Schema matching is a foundational task in enterprise data integration, aiming to align disparate data sources. While traditional methods handle simple one-to-one table mappings, they often struggle with complex multi-table schema matching in real-world applications. We present LLMatch, a unified and modular schema matching framework. LLMatch decomposes schema matching into three distinct stages: schema preparation, table-candidate selection, and column-level alignment, enabling component-level evaluation and future-proof compatibility. It includes a novel two-stage optimization strategy: a Rollup module that consolidates semantically related columns into higher-order concepts, followed by a Drilldown module that re-expands these concepts for fine-grained column mapping. To address the scarcity of complex semantic matching benchmarks, we introduce SchemaNet, a benchmark derived from real-world schema pairs across three enterprise domains, designed to capture the challenges of multi-table schema alignment in practical settings. Experiments demonstrate that LLMatch significantly improves matching accuracy in complex schema matching settings and substantially boosts engineer productivity in real-world data integration.

Figures

Figures reproduced from arXiv: 2507.10897 by the authors.

**Figure 1.** Toy matching task using traditional methods Before the advent of large language models (LLMs), complex multi-table schema matching was largely intractable [42]. Traditional tools struggled with challenges such as inconsistent naming conventions, data heterogeneity, and the lack of standardized structural representations. As shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Architecture diagram of LLMatch. Schema preparation organises information into three key categories for downstream processing. Table selection maps target tables to source tables. Column matching performs column-level mapping. 1.Input Schema source.updated_at source.created_at source.acc_bal source.bal_currency … target.bal_amt target.currency target.currence_code target.currency_id target.acc_cateogry source.acc_ba… view at source ↗

**Figure 3.** Example of multi-level matching with Rollup and Drilldown Recognizing the lack of large-scale, semantically rich benchmarks for schema matching, we curated SchemaNet, a comprehensive benchmark comprising seven datasets across the finance, healthcare, and entertainment domains. Developed in collaboration with two leading financial institutions, SchemaNet captures real-world data integration challenges and encompasse… view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Schema element ablation study: adding contextual information such as PK/FK and descriptions improves the F1 score. 6.6 Schema Elements Ablation Study To evaluate the contribution of schema elements to matching performance, we conducted an ablation study [PITH_FULL_IMA…

**Figure 6.** Figure 6: Rollup/Drilldown improves matching accuracy, with gains up to 40% on some datasets. With Assist Without Assist 0.58 0.14 F1 Score [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 38 canonical work pages

[1]

arXiv preprint arXiv:2303.08774 (2023)

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

arXiv 2023
[2]

Ackerman, R., Gal, A., Sagi, T., Shraga, R.: A cognitive model of human bias in matching. In: PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim In- ternational Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26–30, 2019, Proceedings, Part I 16. Springer (2019)

work page 2019
[3]

International Journal of Advanced Computer Science and Applications8(10) (2017)

Alwan, A.A., Nordin, A., Alzeber, M., Abualkishik, A.Z.: A survey of schema matching research using database schemas and instances. International Journal of Advanced Computer Science and Applications8(10) (2017)

work page 2017
[4]

Anadkat, S.: How to make your completions outputs consistent with the new seed parameter (2023),https://cookbook.openai.com/examples/reproducible_ outputs_with_the_seed_parameter, openAI Cookbook

work page 2023
[5]

Expert Systems with Applications229 (2023)

Asif-Ur-Rahman,M.,Hossain,B.A.,Bewong,M.,Islam,M.Z.,Zhao,Y.,Groves,J., Judith, R.: A semi-automated hybrid schema matching framework for vegetation data integration. Expert Systems with Applications229 (2023)

work page 2023
[6]

Proceedings of the VLDB Endowment4(11) (2011)

Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching, ten years later. Proceedings of the VLDB Endowment4(11) (2011)

work page 2011
[7]

In: 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy (2021)

Cappuzzo, R., Papotti, P., Thirumuruganathan, S.: Embdi: generating embeddings for relational data integration. In: 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy (2021)

work page 2021
[8]

github.io/ETL-LambdaBuilder/, accessed: 2025-03-01

Development, J.R..: Etl lambdabuilder documentation (2024), https://ohdsi. github.io/ETL-LambdaBuilder/, accessed: 2025-03-01

work page 2024

Show all 43 references

[9]

In: VLDB’02: Proceedings of the 28th International Conference on Very Large Databases

Do, H.H., Rahm, E.: Coma—a system for flexible combination of schema matching approaches. In: VLDB’02: Proceedings of the 28th International Conference on Very Large Databases. Elsevier (2002)

2002
[10]

In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

Feng, J., Hong, X., Qu, Y.: An instance-based schema matching method with attributes ranking and classification. In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. vol. 5. IEEE (2009)

2009
[11]

Fernandez, R.C., Elmore, A.J., Franklin, M.J., Krishnan, S., Tan, C.: How large language models will disrupt data management. Proc. VLDB Endow. 16(11) (Jul 2023). https://doi.org/10.14778/3611479.3611527, https://doi.org/10. 14778/3611479.3611527

2023
[12]

In: 2018 IEEE 34th International Con- ference on Data Engineering (ICDE)

Fernandez, R.C., Mansour, E., Qahtan, A.A., Elmagarmid, A., Ilyas, I., Madden, S., Ouzzani, M., Stonebraker, M., Tang, N.: Seeping semantics: Linking datasets using word embeddings for data discovery. In: 2018 IEEE 34th International Con- ference on Data Engineering (ICDE). IE...

2018
[13]

https://www.ft.com/content/ 4138876c-5a10-46d4-b6c6-7d421cbd9df0 (2025), accessed: 2025-03-01

Financial Times: Deutsche Bank struggles with fallout after huge Post- bank IT migration: Regulator makes unprecedented rebuke as many clients locked out of their accounts for weeks. https://www.ft.com/content/ 4138876c-5a10-46d4-b6c6-7d421cbd9df0 (2025), accessed: 2025-03-01

2025
[14]

In: The Semantic Web: Research and Applica- tions: First European Semantic Web Symposium, ESWS 2004 Heraklion, Crete, Greece, May 10-12, 2004

Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an imple- mentation of semantic matching. In: The Semantic Web: Research and Applica- tions: First European Semantic Web Symposium, ESWS 2004 Heraklion, Crete, Greece, May 10-12, 2004. Proceedings 1. Sprin...

2004
[15]

Proceedings of the VLDB Endowment

Huang, Z., Guo, J., Wu, E.: Transform table to database using large language models. Proceedings of the VLDB Endowment. ISSN2150 (2024) 12 S. Wang et al

2024
[16]

https://www.iso.org/standard/55005.html, accessed: 2025-05-07

International Organization for Standardization: ISO 20022-1:2013 – Financial services – Universal financial industry message scheme – Part 1: Metamodel. https://www.iso.org/standard/55005.html, accessed: 2025-05-07

2013
[17]

In: EDBT/ICDT Workshops (2020)

Koutras, C., Fragkoulis, M., Katsifodimos, A., Lofi, C.: Rema: Graph embeddings- based relational schema matching. In: EDBT/ICDT Workshops (2020)

2020
[18]

In: ICDE

Koutras, C., Siachamis, G., Ionescu, A., Psarakis, K., Brons, J., Fragkoulis, M., Lofi, C., Bonifati, A., Katsifodimos, A.: Valentine: Evaluating matching techniques for dataset discovery. In: ICDE. IEEE (2021)

2021
[19]

https://github.com/knowledge-fusion/ LLMatch (2025)

LLMatch: Llmatch code and dataset. https://github.com/knowledge-fusion/ LLMatch (2025)

2025
[20]

In: vldb

Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: vldb. vol. 1 (2001)

2001
[21]

International Arab Journal of Information Technology (IAJIT)14(5) (2017)

Mehdi, O., Ibrahim, H., Affendey, L.: An approach for instance based schema matching with google similarity and regular expression. International Arab Journal of Information Technology (IAJIT)14(5) (2017)

2017
[22]

In: Proceedings 18th international conference on data engineering

Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings 18th international conference on data engineering. IEEE (2002)

2002
[23]

In: 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T)

Munir, S., Khan, F., Riaz, M.A.: An instance based schema matching between opaque database schemas. In: 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T). IEEE (2014)

2014
[24]

OHDSI: Observational Health Data Sciences and Informatics (2019)

2019
[25]

OpenAI: Gpt-3.5 turbo fine-tuning and api updates.https://openai.com/index/ gpt-3-5-turbo-fine-tuning-and-api-updates/ (August 22 2023), retrieved Feb 12, 2025

2023
[26]

Journal of the American Medical Informatics Association19(1) (2012)

Overhage, J.M., Ryan, P.B., Reich, C.G., Hartzema, A.G., Stang, P.E.: Validation of a common data model for active safety surveillance research. Journal of the American Medical Informatics Association19(1) (2012)

2012
[27]

Energy Informatics6(Suppl 1) (2023)

Pan, Z., Yang, M., Monti, A.: Schema matching based on energy domain pre- trained language model. Energy Informatics6(Suppl 1) (2023)

2023
[28]

Joint Workshops at the 50th International Conference on Very Large Data Bases (VLDBW’24) — TaDA’24: 2nd International Workshop on Tabular Data Analysis (2024)

Parciak, M., Vandevoort, B., Neven, F., Peeters, L.M., Vansummeren, S.: Schema matching with large language models: An experimental study. Joint Workshops at the 50th International Conference on Very Large Data Bases (VLDBW’24) — TaDA’24: 2nd International Workshop on Tabular ...

2024
[29]

JMIR Medical Informatics 9(12) (2021)

Paris, N., Lamer, A., Parrot, A.: Transformation and evaluation of the mimic database in the omop common data model: development and usability study. JMIR Medical Informatics 9(12) (2021)

2021
[30]

Journal of the American Medical Informatics Association31(3) (2024)

Reich, C., Ostropolets, A., Ryan, P., Rijnbeek, P., Schuemie, M., Davydov, A., Dymshyts, D., Hripcsak, G.: Ohdsi standardized vocabularies—a large-scale cen- tralized reference ontology for international data harmonization. Journal of the American Medical Informatics Associati...

2024
[31]

In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing

Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert- networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (11 2019)

2019
[32]

In: The Semantic Web–ISWC 2012: 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I 11

Rong, S., Niu, X., Xiang, E.W., Wang, H., Yang, Q., Yu, Y.: A machine learning approach for instance matching based on similarity metrics. In: The Semantic Web–ISWC 2012: 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I 11....

2012
[33]

arXiv preprint arXiv:2406.06608 (2024) LLMATCH: a Unified Schema Matching Framework with LLM 13

Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., Li, Y., Gupta, A., Han, H., Schulhoff, S., et al.: The prompt report: A systematic survey of prompting techniques. arXiv preprint arXiv:2406.06608 (2024) LLMATCH: a Unified Schema Matching Framework with LLM 13

2024 arXiv
[34]

arXiv preprint arXiv:2403.01567 (2024)

Sheetrit, E., Brief, M., Mishaeli, M., Elisha, O.: Rematch: Retrieval enhanced schema matching with llms. arXiv preprint arXiv:2403.01567 (2024)

2024 arXiv
[35]

In: 2021 IEEE 37th International Conference on Data Engineering (ICDE)

Shraga, R., Amir, O., Gal, A.: Learning to characterize matching experts. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE (2021)

2021
[36]

Proceedings of the VLDB En- dowment 13(9) (2020)

Shraga, R., Gal, A., Roitman, H.: Adnev: Cross-domain schema matching using deep similarity matrix adjustment and evaluation. Proceedings of the VLDB En- dowment 13(9) (2020)

2020
[37]

Data & Knowledge Engineering69(12) (2010)

Sorrentino, S., Bergamaschi, S., Gawinecki, M., Po, L.: Schema label normalization for improving schema matching. Data & Knowledge Engineering69(12) (2010)

2010
[38]

Annals of internal medicine153(9) (2010)

Stang, P.E., Ryan, P.B., Racoosin, J.A., Overhage, J.M., Hartzema, A.G., Reich, C., Welebob, E., Scarnecchia, T., Woodcock, J.: Advancing the science for active surveillance: rationale and design for the observational medical outcomes partner- ship. Annals of internal medicine...

2010
[39]

Proceedings of the ACM on Management of Data1(1) (2023)

Tu, J., Fan, J., Tang, N., Wang, P., Li, G., Du, X., Jia, X., Gao, S.: Unicorn: A unified multi-tasking model for supporting matching tasks in data integration. Proceedings of the ACM on Management of Data1(1) (2023)

2023
[40]

Proceedings of the VLDB Endowment 17(12) (2024)

Wornow, D., Suh, G., Elmore, A.J., Krishnan, S., Parameswaran, A.: Automat- ing the enterprise with foundation models. Proceedings of the VLDB Endowment 17(12) (2024)

2024
[41]

In: Advances in Databases and Information Systems: 25th European Conference, ADBIS

Zhang, J., Shin, B., Choi, J.D., Ho, J.C.: Smat: An attention-based deep learning solution to the automation of schema matching. In: Advances in Databases and Information Systems: 25th European Conference, ADBIS. Springer (2021)

2021
[42]

In: International Conference on Data Engineering (ICDE)

Zhang, Y., Floratou, A., Cahoon, J., Krishnan, S., Müller, A.C., Banda, D., Psal- lidas, F., Patel, J.M.: Schema matching using pre-trained language models. In: International Conference on Data Engineering (ICDE). IEEE (2023)

2023
[43]

Data & Knowledge Engineering61(2) (2007)

Zhao, H., Ram, S.: Combining schema and instance information for integrating heterogeneous data sources. Data & Knowledge Engineering61(2) (2007)

2007

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

arXiv preprint arXiv:2303.08774 (2023)

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

arXiv 2023

[2] [2]

Ackerman, R., Gal, A., Sagi, T., Shraga, R.: A cognitive model of human bias in matching. In: PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim In- ternational Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26–30, 2019, Proceedings, Part I 16. Springer (2019)

work page 2019

[3] [3]

International Journal of Advanced Computer Science and Applications8(10) (2017)

Alwan, A.A., Nordin, A., Alzeber, M., Abualkishik, A.Z.: A survey of schema matching research using database schemas and instances. International Journal of Advanced Computer Science and Applications8(10) (2017)

work page 2017

[4] [4]

Anadkat, S.: How to make your completions outputs consistent with the new seed parameter (2023),https://cookbook.openai.com/examples/reproducible_ outputs_with_the_seed_parameter, openAI Cookbook

work page 2023

[5] [5]

Expert Systems with Applications229 (2023)

Asif-Ur-Rahman,M.,Hossain,B.A.,Bewong,M.,Islam,M.Z.,Zhao,Y.,Groves,J., Judith, R.: A semi-automated hybrid schema matching framework for vegetation data integration. Expert Systems with Applications229 (2023)

work page 2023

[6] [6]

Proceedings of the VLDB Endowment4(11) (2011)

Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching, ten years later. Proceedings of the VLDB Endowment4(11) (2011)

work page 2011

[7] [7]

In: 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy (2021)

Cappuzzo, R., Papotti, P., Thirumuruganathan, S.: Embdi: generating embeddings for relational data integration. In: 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy (2021)

work page 2021

[8] [8]

github.io/ETL-LambdaBuilder/, accessed: 2025-03-01

Development, J.R..: Etl lambdabuilder documentation (2024), https://ohdsi. github.io/ETL-LambdaBuilder/, accessed: 2025-03-01

work page 2024