Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T09:45:21.791Z Has data issue: false hasContentIssue false
  • English
  • Français

Alin: improving interactive ontology matching by interactively revising mapping suggestions

Part of: Ontology Alignment: Algorithms and Evaluation

Published online by Cambridge University Press:  20 January 2020

Jomar Da Silva Open the ORCID record for Jomar Da Silva [Opens in a new window] ,
Kate Revoredo ,
Fernanda Baião Open the ORCID record for Fernanda Baião [Opens in a new window]  and
Jérôme Euzenat
Jomar Da Silva
Affiliation:
Graduate Program in Informatics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil e-mails: [email protected], [email protected]
Kate Revoredo
Affiliation:
Graduate Program in Informatics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil e-mails: [email protected], [email protected]
Fernanda Baião
Affiliation:
Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil e-mail: [email protected]
Jérôme Euzenat
Affiliation:
Univ. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e1
Copyright
© Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Algergawy, A., Cheatham, M., Faria, D., Ferrara, A., Fundulaki, I., Harrow, I., Hertling, S., Jiménez-Ruiz, E., Karam, N., Khiat, A., Lambrix, P., Li, H., Montanelli, S., Paulheim, H., Pesquita, C., Saveta, T., Schmidt, D., Shvaiko, P., Splendiani, A., Thiéblin, E., Trojahn, C., Vataščinová, J., Zamazal, O. & Zhou, L. 2018. Results of the ontology alignment evaluation initiative 2018. CEUR Workshop Proceedings 2288, 76–116. http://www.dit.unitn.it/pavel/om2018/papers/oaei18_paper0.pdfGoogle Scholar
Balasubramani, B., Taheri, A. & Cruz, I. 2015. User involvement in ontology matching using an online active learning approach. In CEUR Workshop Proceedings, 1545, 45–49. CEUR-WSGoogle Scholar
Buscher, G., Gwizdka, J., Teevan, J., Belkin, N. J., Bierig, R., van Elst, L. & Jose, J. 2009. SIGIR 2009 workshop on understanding the user: logging and interpreting user interactions in information search and retrieval (workshop report). ACM SIGIR Forum, 43(2), 57–62. http://dx.doi.org/10.1145/1670564.1670574CrossRefGoogle Scholar
Cheatham, M. & Hitzler, P. 2013. String similarity metrics for ontology alignment. In Proceedings of the 12th International Semantic Web Conference – Part II, ISWC 2013, 294–309. Springer-Verlag New York, Inc., New York, NY, USA.CrossRefGoogle Scholar
Chunhua, L., Zhiming, C., Pengpeng, Z., Jian, W., Jie, X. & Tianxu, H. 2015. Improving ontology matching with propagation strategy and user feedback. In Seventh International Conference on Digital Image Processing (ICDIP 2015), 9631, 6. https://doi.org/10.1117/12.2197167CrossRefGoogle Scholar
Cruz, I. F., Loprete, F., Palmonari, M., Stroe, C. & Taheri, A. 2014. Pay-as-you-go multi-user feedback model for ontology matching. In Knowledge Engineering and Knowledge Management, Janowicz, K., Schlobach, S., Lambrix, P. & Hyvönen, E. (eds). Springer International Publishing, Cham, 80–96.Google Scholar
Cruz, I. F., Stroe, C. & Palmonari, M. 2012. Interactive user feedback in ontology matching using signature vectors. In 2012 IEEE 28th International Conference on Data Engineering, 1321–1324.Google Scholar
Da Silva, J., Revoredo, K. & Baião, F. A. 2018. ALIN results for OAEI 2018. http://www.dit.unitn.it/pavel/om2018/papers/oaei18_paper1.pdfGoogle Scholar
Da Silva, J., Revoredo, K., Baião, F. A. & Euzenat, J. 2017. Semantic interactive ontology matching: Synergistic combination of techniques to improve the set of candidate correspondences. In OM 2017 - 12th ISWC Workshop on Ontology Matching, 2032, 13–24. http://disi.unitn.it/pavel/om2017/papers/om2017_Tpaper2.pdfGoogle Scholar
Da Silva, J., Revoredo, K., Baião, F. A. & Euzenat, J. 2018. Interactive Ontology Matching: Using Expert Feedback to Select Attribute Mappings. http://disi.unitn.it/pavel/om2018/papers/om2018_LTpaper3.pdfGoogle Scholar
David, J., Euzenat, J., Scharffe, F. & Trojahn dos Santos, C. 2011. The alignment API 4.0. Semant. Web 2(1), 3–10. http://dl.acm.org/citation.cfm?id=2019470.2019474CrossRefGoogle Scholar
Duan, S., Fokoue, A. & Srinivas, K. 2010. One size does not fit all: Customizing ontology alignment using user feedback. In The Semantic Web – ISWC 2010, Patel-Schneider, P. F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J. Z., Horrocks, I. & Glimm, B. (eds). Springer Berlin Heidelberg, Berlin, Heidelberg, 177–192.Google Scholar
Euzenat, J. & Shvaiko, P. (2013). Ontology Matching, 2nd edition. Springer-Verlag.CrossRefGoogle Scholar
Faria, D. 2016. Using the SEALS Client’s Oracle in Interactive Matching’. https://github.com/DanFaria/OAEI_SealsClient/blob/master/OracleTutorial.pdfGoogle Scholar
Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I. F. & Couto, F. M. 2013. The AgreementMakerLight ontology matching system. In On the Move to Meaningful Internet Systems: OTM 2013 Conferences, Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., De Leenheer, P. & Dou, D. (eds). Springer Berlin Heidelberg, Berlin, Heidelberg, 527–541.Google Scholar
Fellbaum, C., ed. 1998. WordNet: An Electronic Lexical Database. MIT Press.CrossRefGoogle Scholar
Gale, D. & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly 69(1), 9–15. https://doi.org/10.1080/00029890.1962.11989827CrossRefGoogle Scholar
Guedes, A., Baião, F. & Revoredo, K. 2014 a. Digging ontology correspondence antipatterns. In Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP 2014) Co-located with the 13th International Semantic Web Conference (ISWC 2014), 1032, 38–48.Google Scholar
Guedes, A., Baião, F. & Revoredo, K. 2014 b. On the identification and representation of ontology correspondence antipatterns. In Proceedings of the 8th International Workshop on Modular Ontologies co-located with the 8th International Conference on Formal Ontology in Information Systems (FOIS 2014), CEUR Workshop Proceedings, 1248.Google Scholar
Hertling, S. 2012. Hertuda results for OAEI 2012, In Proceedings of the 7th International Conference on Ontology Matching, OM’12, 946. CEUR-WS.org, Aachen, Germany, Germany, 141–144. http://dl.acm.org/citation.cfm?id=2887596.2887607Google Scholar
Irving, R. W., Manlove, D. F. & O’Malley, G. 2009, Stable marriage with ties and bounded length preference lists. Journal of Discrete Algorithms 7(2), 213–219. Selected papers from the 2nd Algorithms and Complexity in Durham Workshop ACiD 2006. http://www.sciencedirect.com/science/article/pii/S1570866708000683CrossRefGoogle Scholar
Jean-Mary, Y. R., Shironoshita, E. P. & Kabuka, M. R. 2009. Ontology Matching with Semantic Verification.CrossRefGoogle Scholar
Jiménez-Ruiz, E. & Cuenca Grau, B. 2011, LogMap: Logic-based and scalable ontology matching. In The Semantic Web – ISWC 2011, Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N. & Blomqvist, E. (eds). Springer Berlin Heidelberg, Berlin, Heidelberg, 273–288.Google Scholar
Jiménez-Ruiz, E., Cuenca Grau, B., Zhou, Y. & Horrocks, I. 2012. Large-scale interactive ontology matching: Algorithms and implementation. In ECAI 2012 – 20th European Conference on Artificial Intelligence, 242, 444–449.Google Scholar
Lambrix, P. & Kaliyaperumal, R. 2016. A session-based ontology alignment approach enabling user involvement. Semantic Web 1, 1–28.Google Scholar
Lastra-Díaz, J. J., García-Serrano, A., Batet, M., Fernández, M. & Chirigati, F. 2017. HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset. Inf. Syst. 66(C), 97–118. https://doi.org/10.1016/j.is.2017.02.002CrossRefGoogle Scholar
Lopes, V., Baião, F. & Revoredo, K. 2015. Alinhamento Interativo de Ontologias Uma Abordagem Baseada em Query-by-Committee, Master’s thesis, UNIRIO.Google Scholar
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J. & McClosky, D. 2014. The Stanford CoreNLP natural language processing toolkit. In Association for Computational Linguistics (ACL) System Demonstrations, 55–60. http://www.aclweb.org/anthology/P/P14/P14-5010CrossRefGoogle Scholar
Ngo, D., Bellahsene, Z. & Todorov, K. 2013. Opening the black box of ontology matching. In: The Semantic Web: Semantics and Big Data, Cimiano, P., Corcho, O., Presutti, V., Hollink, L. & Rudolph, S. (eds). Springer Berlin Heidelberg, Berlin, Heidelberg, 16–30.Google Scholar
Paulheim, H. & Hertling, S. 2013. WeSeE-match results for OAEI 2013. In Proceedings of the 8th International Conference on Ontology Matching, OM’13, 1111. CEUR-WS.org, Aachen, Germany, Germany, 197–202. http://dl.acm.org/citation.cfm?id=2874493.2874513Google Scholar
Paulheim, H., Hertling, S. & Ritze, D. 2013, Towards evaluating interactive ontology matching tools. In Lecture Notes in Computer Science 7882, 31–45.CrossRefGoogle Scholar
Petrakis, E. G. M., Varelas, G., Hliaoutakis, A. & Raftopoulou, P. 2006. Design and evaluation of semantic similarity measures for concepts stemming from the same or different ontologies object instrumentality. In Proceedings of the 4th Workshop on Multimedia Semantics (WMS) 4, 233–237.Google Scholar
Shi, F., Li, J., Tang, J., Xie, G. & Li, H. 2009, Actively learning ontology matching via user interaction. In The Semantic Web – ISWC 2009, Bernstein, A., Karger, D. R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E. & Thirunarayan, K. (eds). Springer Berlin Heidelberg, Berlin, Heidelberg, 585–600.Google Scholar
Surhone, L. M., Timpledon, M. T. & Marseken, S. F. 2010. SimMetrics. VDM Publishing.Google Scholar
To, H., I. R. & Le, H. 2009. An adaptive machine learning framework with user interaction for ontology matching. In Twenty-first International Joint Conference on Artificial Intelligence.Google Scholar