Skip to main content
Springer Nature Link
Log in

LLMs4OM: Matching Ontologies with Large Language Models

  • Conference paper
  • First Online:
The Semantic Web: ESWC 2024 Satellite Events (ESWC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15344))

Included in the following conference series:

  • 56 Accesses

Abstract

Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of LLMs in OM tasks. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. Through comprehensive evaluations using 20 OM datasets from various domains, we demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. Our results highlight the potential of LLMs to significantly contribute to the field of OM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Algergawy, A., Babalou, S., Klan, F., König-Ries, B.: Ontology modularization with OAPT. J. Data Semant. 9(2), 53–83 (2020). https://doi.org/10.1007/s13740-020-00114-7, https://doi.org/10.1007/s13740-020-00114-7

  2. Almazrouei, E., et al.: The falcon series of open language models (2023)

    Google Scholar 

  3. Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72–78. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019). https://doi.org/10.18653/v1/W19-1909, https://www.aclweb.org/anthology/W19-1909

  4. Amir, M., et al.: Truveta Mapper: a zero-shot ontology alignment framework (2023)

    Google Scholar 

  5. Cer, D., et al.: Universal sentence encoder (2018)

    Google Scholar 

  6. Chung, H.W., et al.: Scaling instruction-finetuned language models (2022)

    Google Scholar 

  7. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)

    Google Scholar 

  8. Dragisic, Z., Ivanova, V., Li, H., Lambrix, P.: Experiences from the anatomy track in the ontology alignment evaluation initiative. J. Biomed. Semant. 8(1), 56 (2017). https://doi.org/10.1186/s13326-017-0166-5

  9. Efeoglu, S.: GraphMatcher: a graph representation learning approach for ontology matching. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3324, pp. 174–180. CEUR-WS.org (2022)

    Google Scholar 

  10. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer Publishing Company, Incorporated, 2nd edn. (2013). https://doi.org/10.1007/978-3-642-38721-0

  11. Euzenat, J., Meilicke, C., Stuckenschmidt, H., Shvaiko, P., Trojahn, C.: Ontology alignment evaluation initiative: six years of experience. J. Data Semant. 15, 158–192 (2011). https://doi.org/10.1007/978-3-642-22630-4_6

  12. Fallatah, O., Zhang, Z., Hopfgartner, F.: A gold standard dataset for large knowledge graphs matching (2020). https://eprints.whiterose.ac.uk/173366/, 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0)

  13. Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I.F., Couto, F.M.: The AgreementMakerLight ontology matching system. In: Meersman, R., et al. (eds.) OTM 2013. LNCS, vol. 8185, pp. 527–541. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41030-7_38

    Chapter  Google Scholar 

  14. Faria, D., Silva, M.C., Cotovio, P., Ferraz, L., Balbi, L., Pesquita, C.: Results for Matcha and Matcha-DL in OAEI 2023. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 164–169. CEUR-WS.org (2023)

    Google Scholar 

  15. Gosselin, F., Zouaq, A.: SORBET: a Siamese network for ontology embeddings using a distance-based regression loss and BERT. In: Payne, T.R., et al. (eds.) The Semantic Web - ISWC 2023, pp. 561–578. Springer Nature Switzerland, Cham (2023)

    Chapter  MATH  Google Scholar 

  16. Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)

  17. Harrow, I., et al.: Matching disease and phenotype ontologies in the ontology alignment evaluation initiative. J. Biomed. Semant. 8(1), 55 (2017). https://doi.org/10.1186/s13326-017-0162-9, https://doi.org/10.1186/s13326-017-0162-9

  18. He, Y., Chen, J., Antonyrajah, D., Horrocks, I.: BERTMap: A BERT-based ontology alignment system (2022)

    Google Scholar 

  19. He, Y., Chen, J., Dong, H., Horrocks, I.: Exploring large language models for ontology alignment (2023)

    Google Scholar 

  20. He, Y., Chen, J., Dong, H., Jiménez-Ruiz, E., Hadian, A., Horrocks, I.: Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching. In: Sattler, U., et al. (eds.) The Semantic Web - ISWC 2022, pp. 575–591. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-19433-7_33

    Chapter  Google Scholar 

  21. Hertling, S., Paulheim, H.: OLaLa: ontology matching with large language models. In: Proceedings of the 12th Knowledge Capture Conference 2023, pp. 131–139. K-CAP ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3587259.3627571

  22. Jiang, A.Q., et al.: Mistral 7B (2023)

    Google Scholar 

  23. Jiménez-Ruiz, E., Cuenca Grau, B.: LogMap: logic-based and scalable ontology matching. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 273–288. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_18

    Chapter  MATH  Google Scholar 

  24. Karam, N., Khiat, A., Algergawy, A., Sattler, M., Weiland, C., Schmidt, M.: Matching biodiversity and ecology ontologies: challenges and evaluation results. Knowl. Eng. Rev. 35, e9 (2020). https://doi.org/10.1017/S0269888920000132, https://doi.org/10.1017/S0269888920000132

  25. Labrak, Y., Bazoge, A., Morin, E., Gourraud, P.A., Rouvier, M., Dufour, R.: BioMistral: a collection of open-source pretrained large language models for medical domains (2024)

    Google Scholar 

  26. Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks (2021)

    Google Scholar 

  27. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing (2021)

    Google Scholar 

  28. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019)

    Google Scholar 

  29. Nas, E., Huschka, M.: MSE Benchmark. https://github.com/EngyNasr/MSE-Benchmark (2023)

  30. Norouzi, S.S., Mahdavinejad, M.S., Hitzler, P.: Conversational ontology alignment with ChatGPT. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 61–66. CEUR-WS.org (2023)

    Google Scholar 

  31. Noy, N., Mcguinness, D.: Ontology development 101: a guide to creating your first ontology. Knowl. Syst. Lab. 32 (2001)

    Google Scholar 

  32. OpenAI: ChatGPT. https://openai.com/chat-gpt/ (2023). Accessed 5 May 2023

  33. OpenAI: new and improved embedding model (2023). https://openai.com/blog/new-and-improved-embedding-model. Retrieved 15 Dec 2022

  34. Osman, I., Ben Yahia, S., Diallo, G.: Ontology integration: approaches and challenging issues. Inf. Fus. 71, 38–63 (2021). https://doi.org/10.1016/j.inffus.2021.01.007

    Article  Google Scholar 

  35. Peng, Y., Alam, M., Bonald, T.: Ontology matching using textual class descriptions. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 67–72. CEUR-WS.org (2023)

    Google Scholar 

  36. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1162, https://aclanthology.org/D14-1162

  37. Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using Siamese BERT-networks (2019)

    Google Scholar 

  38. Sammut, C., Webb, G.I. (eds.): TF–IDF, pp. 986–987. Springer US, Boston, MA (2010). https://doi.org/10.1007/978-0-387-30164-8_832

  39. Sharma, A., Jain, S.: LSMatch and LSMatch-multilingual results for OAEI 2023. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 159–163. CEUR-WS.org (2023)

    Google Scholar 

  40. Shvaiko, P., Euzenat, J., Jiménez-Ruiz, E., Hassanzadeh, O., Trojahn, C. (eds.): Proceedings of the 18th International Workshop on Ontology Matching co-located with the 22nd International Semantic Web Conference (ISWC 2023), Athens, Greece, November 7, 2023, CEUR Workshop Proceedings, vol. 3591. CEUR-WS.org (2023)

    Google Scholar 

  41. da Silva, J., Revoredo, K., Baião, F., Lima, C.: ALIN results for OAEI 2023. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 140–145. CEUR-WS.org (2023)

    Google Scholar 

  42. Singh, A., D’Arcy, M., Cohan, A., Downey, D., Feldman, S.: SciRepEval: a multi-format benchmark for scientific document representations. ArXiv abs/2211.13308 (2022)

    Google Scholar 

  43. Sousa, G., Lima, R., Trojahn, C.: Combining word and sentence embeddings with alignment extension for property matching. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 91–96. CEUR-WS.org (2023)

    Google Scholar 

  44. Stephan, G., Pascal, H., Andreas, A.: Knowledge Representation and Ontologies, pp. 51–105. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/3-540-70894-4_3

  45. Team, M.N.: Introducing MPT-7B: a new standard for open-source, commercially usable LLMs (2023). www.mosaicml.com/blog/mpt-7b. Accessed 05 May 2023

  46. Touvron, H., at el.: Llama 2: open foundation and fine-tuned chat models (2023)

    Google Scholar 

  47. Wang, Z.: AMD results for OAEI 2023. In: OM@ISWC. CEUR Workshop Proceedings, vol. 3591, pp. 146–153. CEUR-WS.org (2023)

    Google Scholar 

  48. Wang, Z.: Contextualized structural self-supervised learning for ontology matching (2023)

    Google Scholar 

  49. Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing (2020)

    Google Scholar 

  50. Xue, L., et al.: ByT5: towards a token-free future with pre-trained byte-to-byte models (2022)

    Google Scholar 

  51. Zhang, X., Zhao, C., Wang, X.: A survey on knowledge representation in materials science and engineering: an ontological perspective. Computers in Industry 73, 8–22 (2015). https://doi.org/10.1016/j.compind.2015.07.005

  52. Zheng, L., et al.: Judging LLM-as-a-judge with MT-bench and chatbot arena (2023)

    Google Scholar 

Download references

Acknowledgments

We thank Nenad Krdzavac for valuable insights on a previous draft of this paper. This work was supported by the German BMBF project SCINEXT (ID 01lS22070), the European Research Council for ScienceGRAPH (GA ID: 819536), and German DFG for NFDI4DataScience (no. 460234259).

Author information

Authors and Affiliations

  1. TIB Leibniz Information Centre for Science and Technology, Hannover, Germany

    Hamed Babaei Giglou, Jennifer D’Souza, Felix Engel & Sören Auer

Authors
  1. Hamed Babaei Giglou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer D’Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felix Engel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sören Auer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Babaei Giglou .

Editor information

Editors and Affiliations

  1. King’s College London, London, UK

    Albert Meroño Peñuela

  2. Universidad Politécnica de Madrid, Madrid, Spain

    Oscar Corcho

  3. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  4. King’s College London, London, UK

    Elena Simperl

  5. University of Liverpool, Liverpool, UK

    Valentina Tamma

  6. National Research Council, Bologna, Italy

    Andrea Giovanni Nuzzolese

  7. Universidad Politécnica de Madrid, Madrid, Spain

    Maria Poveda-Villalón

  8. Vienna University of Economics and Business, Vienna, Austria

    Marta Sabou

  9. University of Bologna, Bologna, Italy

    Valentina Presutti

  10. Cefriel - Politecnico Di Milano, Milan, Italy

    Irene Celino

  11. Semantic Web Company, Vienna, Austria

    Artem Revenko

  12. University of Paris-Saclay, Gif-sur-Yvette, France

    Joe Raad

  13. Ludwig-Maximilians-Universität München, Munich, Germany

    Bruno Sartini

  14. EURECOM, Biot, France

    Pasquale Lisena

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Babaei Giglou, H., D’Souza, J., Engel, F., Auer, S. (2025). LLMs4OM: Matching Ontologies with Large Language Models. In: Meroño Peñuela, A., et al. The Semantic Web: ESWC 2024 Satellite Events. ESWC 2024. Lecture Notes in Computer Science, vol 15344. Springer, Cham. https://doi.org/10.1007/978-3-031-78952-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78952-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78951-9

  • Online ISBN: 978-3-031-78952-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics