A Lightweight Explainable Framework for Schema Matching and Data Fusion in Low-Code Data Engineering Pipelines

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Abstract

Low-code data engineering environments increasingly support rapid data ingestion, transformation, and integration, yet schema matching remains a persistent challenge when heterogeneous datasets use inconsistent naming conventions, limited metadata, and weak documentation. This study proposes a lightweight explainable framework for schema matching and data fusion in low-code data engineering pipelines. The framework integrates sentence embedding similarity, fuzzy string matching, keyword-based heuristics, and data-type compatibility validation to identify candidate correspondences between independently structured tabular schemas without requiring labeled training data, predefined ontologies, or extensive manual mapping. The approach is evaluated using publicly available Airbnb Listings and Reviews datasets, where the Listings file contains property-level attributes and the Reviews file contains review-event attributes, creating a practical case for schema alignment across related but differently structured data sources. In addition to hybrid similarity scoring, the framework includes an optional large language model based justification layer that generates natural-language explanations for predicted column matches, improving interpretability for analysts, data engineers, and low-code users. The experimental results show that a moderate similarity threshold of 0.65 achieved a precision of 0.50, recall of 0.33, and F1-score of 0.40, while a stricter threshold of 0.80 achieved a precision of 1.00, recall of 0.25, and F1-score of 0.40. These findings demonstrate the trade-off between broader mapping coverage and high-confidence matching, while confirming the value of explainable scoring for low-supervision schema alignment. The proposed framework contributes a domain-agnostic and low-footprint method for preparing heterogeneous data sources for downstream data fusion, ETL automation, and analytical workflows in low-code data engineering settings.

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1. [1] Rahm, E., & Bernstein, P. A. (2001). A survey of approaches to automatic schema matching. The VLDB Journal, 10(4), 334–350. https://doi.org/10.1007/s007780100057
2. [2] Doan, A., Domingos, P., & Halevy, A. Y. (2001). Reconciling schemas of disparate data sources: A machine-learning approach. Proceedings of the ACM SIGMOD International Conference on Management of Data, 509–520. https://doi.org/10.1145/375663.375731
3. [3] Melnik, S., Garcia-Molina, H., & Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. Proceedings of the 18th International Conference on Data Engineering, 117–128. https://doi.org/10.1109/ICDE.2002.994702
4. [4] Giunchiglia, F., & Shvaiko, P. (2004). Semantic matching. The Knowledge Engineering Review, 18(3), 265–280. https://doi.org/10.1017/S0269888904000074
5. [5] Aumueller, D., Do, H. H., Massmann, S., & Rahm, E. (2005). Schema and ontology matching with COMA++. Proceedings of the ACM SIGMOD International Conference on Management of Data, 906–908. https://doi.org/10.1145/1066157.1066283
6. [6] Bilke, A., & Naumann, F. (2005). Schema matching using duplicates. Proceedings of the 21st International Conference on Data Engineering, 69–80. https://doi.org/10.1109/ICDE.2005.126
7. [7] Halevy, A. Y., Ashish, N., Bitton, D., Carey, M. J., Draper, D., Pollock, J., Rosenthal, A., & Sikka, V. (2005). Enterprise information integration: Successes, challenges and controversies. Proceedings of the ACM SIGMOD International Conference on Management of Data, 778–787. https://doi.org/10.1145/1066157.1066246
8. [8] Do, H. H., & Rahm, E. (2007). Matching large schemas: Approaches and evaluation. Information Systems, 32(6), 857–885. https://doi.org/10.1016/j.is.2006.09.002
9. [9] Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16. https://doi.org/10.1109/TKDE.2007.250581
10. [10] Bleiholder, J., & Naumann, F. (2008). Data fusion. ACM Computing Surveys, 41(1), 1–41. https://doi.org/10.1145/1456650.1456651
11. [11] Bernstein, P. A., Madhavan, J., & Rahm, E. (2011). Generic schema matching, ten years later. Proceedings of the VLDB Endowment, 4(11), 695–701. https://doi.org/10.14778/3402707.3402710
12. [12] Shvaiko, P., & Euzenat, J. (2013). Ontology matching: State of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 25(1), 158–176. https://doi.org/10.1109/TKDE.2011.253
13. [13] Cer, D., Yang, Y., Kong, S. Y., Hua, N., Limtiaco, N., St. John, R., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., Strope, B., & Kurzweil, R. (2018). Universal Sentence Encoder for English. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 169–174. https://doi.org/10.18653/v1/D18-2029
14. [14] Konda, P., Das, S., Suganthan, P. G. C., Doan, A., Ardalan, A., Ballard, J. R., Li, H., Panahi, F., Zhang, H., Naughton, J. F., Prasad, S., Krishnan, G., Deep, R., & Raghavendra, V. (2016). Magellan: Toward building entity matching management systems over data science stacks. Proceedings of the VLDB Endowment, 9(13), 1581–1584. https://doi.org/10.14778/3007263.3007314
15. [15] Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., Deep, R., Arcaute, E., & Raghavendra, V. (2018). Deep learning for entity matching: A design space exploration. Proceedings of the ACM SIGMOD International Conference on Management of Data, 19–34. https://doi.org/10.1145/3183713.3196926
16. [16] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42. https://doi.org/10.1145/3236009
17. [17] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 3982–3992. https://doi.org/10.18653/v1/D19-1410
18. [18] Sahay, A., Indamutsa, A., Di Ruscio, D., & Pierantonio, A. (2020). Supporting the understanding and comparison of low-code development platforms. Proceedings of the 46th Euromicro Conference on Software Engineering and Advanced Applications, 171–178. https://doi.org/10.1109/SEAA51224.2020.00036
19. [19] Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence: Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
20. [20] Luo, Y., Liang, P., Wang, C., Shahin, M., & Zhan, J. (2021). Characteristics and challenges of low-code development: The practitioners’ perspective. Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 1–11. https://doi.org/10.1145/3475716.347578

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