SpringerLink
Log in

Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

Ontology alignment plays a key role in the management of heterogeneous data sources and metadata. In this context, various ontology alignment techniques have been proposed to discover correspondences between the entities of different ontologies. This paper proposes a new ontology alignment approach based on a set of rules exploiting the embedding space and measuring clusters of labels to discover the relationship between entities. We tested our system on the OAEI conference complex alignment benchmark track and then applied it to aligning ontologies in a real-world case study. The experimental results show that the combination of word embedding and a measure of dispersion of the clusters of labels, which we call the radius measure, makes it possible to determine, with good accuracy, not only equivalence relations, but also hierarchical relations between entities.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://oaei.ontologymatching.org/

  2. https://www.Silex-france.com/Silex/

  3. http://www.onisep.fr/

  4. https://fasttext.cc/docs/en/pretrained-vectors.html

  5. https://en.wikipedia.org/wiki/Cosine_similarity

  6. https://ec.europa.eu/esco/portal/home

  7. http://www.pole-emploi.org/accueil/mot-cle.html?tagId=94b2eaf6-d7bd-4244-bddc-01415605563b

  8. http://cigref.hr-ingenium.com/accueil.aspx

  9. https://www.insee.fr/fr/information/2406147

  10. https://www.unspsc.org/

References

  1. Alshargi F, Shekarpour S, Soru T, Sheth A (2018a) Concept2vec: metrics for evaluating quality of embeddings for ontological concepts. ar**v:1803.04488

  2. Alshargi F, Shekarpour S, Soru T, Sheth AP (2018b) Metrics for evaluating quality of embeddings for Ontological concepts. Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)

  3. Ardjani F, Bouchiha D, Malki M (2015) Ontology-alignment techniques: survey and analysis. Int J Modern Educ Comput Sci 7:11

  4. Aumueller D, Do HH, Massmann S, Rahm E (2005) Schema and ontology matching with coma++. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pp 906–908

  5. Chen M, Tian Y, Yang M, Zaniolo C (2016) Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. ar**v:1611.03954

  6. Cruz IF, Antonelli FP, Stroe C (2009) Agreementmaker: efficient matching for large real-world schemas and ontologies. Proce VLDB Endow 2(2):1586–1589

    Article  Google Scholar 

  7. David J (2007) Aroma: a method for the discovery of alignments between ontologies from association rules. Ph.D. thesis, Thèse d’informatique. Université de Nantes. Nantes (FR). http://tel.archives-ouvertes.fr/tel-00200040/en

  8. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. ar**. In: International semantic web conference. Springer, pp 683–697

  9. Euzenat J, Valtchev P (2004) Similarity-based ontology alignment in owl-lite. In: Proceedings of the 16th European conference on artificial intelligence (ECAI). IOS press, pp 333–337

  10. Euzenat J, Shvaiko P et al (2007) Ontology matching, vol 18. Springer, Berlin

    MATH  Google Scholar 

  11. Giunchiglia F, Shvaiko P, Yatskevich M (2004) S-match: an algorithm and an implementation of semantic matching. In: European semantic web symposium. Springer, pp 61–75

  12. Giunchiglia F, Yatskevich M, Shvaiko P (2007) Semantic matching: algorithms and implementation. In: Journal on data semantics IX. Springer, pp 1–38

  13. Gracia J, Mena E (2012) Semantic heterogeneity issues on the web. IEEE Internet Comput 16(5):60–67. https://doi.org/10.1109/MIC.2012.116

    Article  Google Scholar 

  14. Gromann D, Declerck T (2018) Comparing pretrained multilingual word embeddings on an ontology alignment task. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)

  15. Hassen W (2012) Medley results for oaei 2012. In: Proceedings of the 7th international conference on ontology matching-volume 946, CEUR-WS. org, pp 168–172

  16. Jean-Mary YR, Shironoshita EP, Kabuka MR (2009) Ontology matching with semantic verification. J Web Seman 7(3):235–251

    Article  Google Scholar 

  17. Jian N, Hu W, Cheng G, Qu Y (2005) Falcon-ao: Aligning ontologies with falcon. In: Proceedings of K-CAP workshop on integrating ontologies, pp 85–91

  18. Jiang S, Lowd D, Kafle S, Dou D (2016) Ontology matching with knowledge rules. In: Transactions on large-scale data-and knowledge-centered systems XXVIII. Springer, pp 75–95

  19. Kalfoglou Y, Schorlemmer M (2003) Ontology map**: the state of the art. Knowl Eng Rev 18(1):1–31

    Article  Google Scholar 

  20. Kolyvakis P, Kalousis A, Kiritsis D (2018) Deepalignment: unsupervised ontology matching with refined word vectors. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, vol 1 (Long Papers), pp 787–798

  21. Lastra-Díaz JJ, Goikoetxea J, Taieb MAH, García-Serrano A, Aouicha MB, Agirre E (2019) A reproducible survey on word embeddings and ontology-based methods for word similarity: linear combinations outperform the state of the art. Eng Appl Artif Intell 85:645–665

    Article  Google Scholar 

  22. Lesk M (1986) Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th annual international conference on systems documentation, Citeseer, pp 24–26

  23. Li J, Tang J, Li Y, Luo Q (2008) Rimom: a dynamic multistrategy ontology alignment framework. IEEE Trans Knowl Data Eng 21(8):1218–1232

    Google Scholar 

  24. Madhavan J, Bernstein PA, Rahm E (2001) Generic schema matching with cupid. In: vldb, Citeseer, vol 1, pp 49–58

  25. Martin L, Muller B, Suárez PJO, Dupont Y, Romary L, de la Clergerie ÉV, Seddah D, Sagot B (2019) Camembert: a tasty french language model. ar**v:1911.03894

  26. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  27. Mohammadi M, Atashin AA, Hofman W, Tan Y (2018) Comparison of ontology alignment systems across single matching task via the Mcnemar’s test. ACM Trans Knowl Discov Data (TKDD) 12(4):51

  28. Monge AE, Elkan C et al (1996) The field matching problem: algorithms and applications. Kdd 2:267–270

    Google Scholar 

  29. Ngo D, Bellahsene Z (2012) Yam++: a multi-strategy based approach for ontology matching task. In: International conference on knowledge engineering and knowledge management. Springer, pp 421–425

  30. Nkisi-Orji I, Wiratunga N, Massie S, Hui KY, Heaven R (2018) Ontology alignment based on word embedding and random forest classification. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 557–572

  31. Noy NF, Musen MA (2001) Anchor-prompt: Using non-local context for semantic matching. In: OIS@ IJCAI

  32. Ochieng P, Kyanda S (2018) Large-scale ontology matching: state-of-the-art analysis. ACM Comput Surv (CSUR) 51(4):75

    Article  Google Scholar 

  33. Otero-Cerdeira L, Rodríguez-Martínez FJ, Gómez-Rodríguez A (2015) Ontology matching: a literature review. Expert Syst Appl 42(2):949–971

    Article  Google Scholar 

  34. Parrochia D, Neuville P (2014) Taxinomie et réalité: vers une métaclassification. ISTE Group

  35. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. ar**v:1802.05365

  36. Rahm E, Bernstein PA (2001) A survey of approaches to automatic schema matching. VLDB J 10(4):334–350

    Article  Google Scholar 

  37. Ristoski P, Faralli S, Ponzetto SP, Paulheim H (2017) Large-scale taxonomy induction using entity and word embeddings. In: Proceedings of the international conference on web intelligence. ACM, pp 81–87

  38. Ritze D, Meilicke C, Šváb-Zamazal O, Stuckenschmidt H (2009) A pattern-based ontology matching approach for detecting complex correspondences. In: ISWC workshop on ontology matching, chantilly (VA US), pp 25–36

  39. Ritze D, Völker J, Meilicke C, Sváb-Zamazal O (2010) Linguistic analysis for complex ontology matching. In: CEUR workshop proceedings, RWTH, vol 689, Paper–1

  40. Schmidt D, Basso R, Trojahn C, Vieira R (2018) Matching domain and top-level ontologies exploring word sense disambiguation and word embedding. In: Ontology matching: OM-2018: proceedings of the ISWC workshop, p 1

  41. Shvaiko P, Euzenat J (2005) A survey of schema-based matching approaches. In: Journal on data semantics IV. Springer, pp 146–171

  42. Shvaiko P, Euzenat J (2011) Ontology matching: state of the art and future challenges. IEEE Trans Knowl Data Eng 25(1):158–176

    Article  Google Scholar 

  43. Sun M, Zhu H, ** entity alignment with knowledge graph embedding. IJCAI 18:4396–4402

    Google Scholar 

  44. Thieblin E (2019) Task-oriented complex alignments on conference organisation

  45. Thiéblin E, Haemmerlé O, Hernandez N, Trojahn C (2017) Un jeu de données d’évaluation de correspondances complexes entre ontologies

  46. Thiéblin É, Haemmerlé O, Hernandez N, Trojahn C (2018) Task-oriented complex ontology alignment: two alignment evaluation sets. In: European semantic web conference. Springer, pp 655–670

  47. Vieira R, Revoredo K (2017) Using word semantics on entity names for correspondence set generation. In: OM@ ISWC, pp 223–224

  48. Zhang Y, Wang X, Lai S, He S, Liu K, Zhao J, Lv X (2014) Ontology matching with word embeddings. In: Chinese computational linguistics and natural language processing based on naturally annotated big data. Springer, pp 34–45

Download references

Author information

Authors and Affiliations

  1. Université Côte d’Azur, Inria, CNRS, I3S, Sophia Antipolis, France

    Molka Tounsi Dhouib, Catherine Faron & Andrea G. B. Tettamanzi

Authors
  1. Molka Tounsi Dhouib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Catherine Faron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea G. B. Tettamanzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Molka Tounsi Dhouib.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tounsi Dhouib, M., Faron, C. & Tettamanzi, A.G.B. Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment. J Data Semant 10, 399–408 (2021). https://doi.org/10.1007/s13740-021-00137-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13740-021-00137-8

Keywords

Search

Navigation