Ontology Matching by Jointly Encoding Terminological Description and Network Structure
Pages 77 - 85
Abstract
Ontology matching is usually performed to find semantic correspondences between the entity elements of different ontologies to enable interoperability. Current research on ontology matching has largely focused on representation learning. However, there still exist two limitations. Firstly, they are only used in the element level matching phase, ignoring relations of the entity. Secondly, the final alignment threshold is usually determined manually within these methods. It is difficult for an expert to adjust the threshold value and even more for non-expert user. To address these issues, we propose an alternative ontology matching framework, which models the matching process by embedding techniques with jointly encoding ontology terminological description and network structure. We further improve our iterative final alignment method by introducing an automatic adjustment of threshold method. Finally, we perform an experimental evaluation and compare it with state-of-the-art ontology matching systems on four Ontology Alignment Evaluation Initiative (OAEI) datasets. Our approach performs better than most of the systems and achieves a competitive performance. Moreover, we obtained F-measure values of 93.8% and 90.8% on the OAEI Large Biomedical Ontologies FMA-NCI and FMA-SNOMED subtasks.
References
[1]
B. Ramis, L. Gonzalez, S. Iarovyi, A. Lobov, J. L. M. Lastra, V. Vyatkin, and W. Dai, "Knowledge-based web service integration for industrial automation," in Proc. INDIN, PortoAlegre, SouthernBrazil, SouthAmerica, 2014, pp. 733--739.
[2]
JO. Bodenreider, "Biomedical ontologies in action: role in knowledge management, data integration and decision support," Yearb. Med. Inform., vol. 17, no. 1, pp. 67--79, Aug. 2008.
[3]
R. Hoehndorf, P. N. Schofield, and G. V. Gkoutos, "The role of ontologies in biological and biomedical research: a functional perspective," Brief. Bioinform., vol. 16, no. 6, pp. 1069--1080, Nov. 2015.
[4]
C. Xie, M. W. Chekol, B. Spahiu, and H. Cai, "Leveraging Structural Information in Ontology Matching," in Proc. AINA, Crans-Montana, Switzerland, 2016, pp. 1108--1115.
[5]
P. Shvaiko, J. Euzenat, "Ontology Matching: State of the Art and Future Challenges," IEEE Transactionson Knowledge and Data Engineering, vol. 25, no. 1, pp. 158--176, Jan. 2013.
[6]
M. Cheatham and C. Pesquita, "Semantic data integration," in Handbook of Big Data Technologies, Springer, 2017, pp. 263--305.
[7]
A. H. Nezhadi, B. Shadgar, and A. Osareh, "ontology alignment using machine learning techniques," Int. J. Comput. Sci. Inf. Technol., vol. 3, no. 2, pp. 139--150, May 2011.
[8]
D. Faria, C. Pesquita, E. Santos, M. Palmonari, I. F. Cruz, and F. M. Couto, "The AgreementMakerLight Ontology Matching System," in Proc. OTM, Springer, Berlin, Heidelberg, 2013, pp. 527--541.
[9]
M. Zhao, S. Zhang, W. Li, and G. Chen, "Matching biomedical ontologies based on formal concept analysis," J. Biomed. Semantics, vol. 9, no. 1, pp. 11, Dec. 2018.
[10]
E. Jiménez-Ruiz and B. C. Grau, "LogMap: Logic-based and Scalable OntologyMatching," in Proc. ISWC, Bonn, Germany, 2011, pp. 273--288.
[11]
W. E. Djeddi, M. T. Khadir, "A novel approach using context-based measure for matching large scale ontologies," in Proc. DaWaK, Munich, Germany, 2014, pp. 320--331.
[12]
P. Kolyvakis, A. Kalousis, and D. Kiritsis, "DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors," in Proc. NAACL HLT, New Orleans, USA, 2018, pp. 787--798.
[13]
P. Kolyvakis, A. Kalousis, B. Smith, and D. Kiritsis, "Biomedicalontology alignment: an approach based on representation learning," J. Biomed. Semantics, vol. 9, no. 1, pp. 21--40, Dec. 2018.
[14]
C. Xiang, T. Jiang, B. Chang, and Z. Sui, "ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation," in Proc. EMNLP, Lisbon, Portugal, 2015, pp. 2419--2429.
[15]
B. Olivier, "The unified medical language system (UMLS): integrating biomedical terminology," Nucleic acids research, vol. 32, Database issue, pp. 267--270, 2004.
[16]
M. Cheatham and P. Hitzler, "String similarity metrics for ontology alignment," in Proc. ISWC, Springer, Berlin, Heidelberg, 2013, pp. 294--309.
[17]
Y. Zhang et al., "Ontology Matching with Word Embeddings" in Proc. NLP-NABD 2014, CCL 2014, Wuhan, China, 2014, pp. 34--45.
[18]
Lucy Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, and Waleed Ammar. Ontology alignment in the biomedical domain using entity definitions and context. In BioNLP, pages 47--55, 2018.
[19]
J. Portisch, "ALOD2Vec Matcher," in Proc. OM, Monterey, CA, USA, 2018, pp. 132--137.
[20]
S. Hertling, H. Paulheim, "DOME results for OAEI 2018," in Proc. OM, Monterey, CA, USA, 2018, pp. 144--151.
[21]
A. Vaswani et al., "Attention Is All You Need," unpublished.
[22]
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, "Graph Attention Networks," presented at ICLR, Vancouver, Canada, Apr. 30-3, 2018.
[23]
M. Gulić and M. Vuković, "An Iterative Automatic Final Alignment Method in the Ontology Matching System," J. Inf. Organ. Sci., vol. 42, no. 1, pp. 39--61, Jun. 2018.
[24]
T. F. Hayamizu, M. Mangan, and J. P. Corradi, "The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data," Genome Biology, vol. 6, no. 3, pp. 29--36, Mar. 2005.
[25]
S. D. Coronado, M. Haber, N. Sioutos et al., "NCI Thesaurus: using science-based terminology to integrate cancer research results," Stud Health Technol Inform, vol. 107, no. 1, pp. 33--37, Feb. 2004.
[26]
C. Rosse, J. L. V. Mejino, "A reference ontology for biomedical informatics: the Foundational Model of Anatomy," J. Biomed. Inform., vol. 36, no. 6, pp. 478--500, Dec. 2003.
[27]
K. Donnelly, "SNOMED-CT: The advanced terminology and coding system for eHealth," Stud Health Technol Inform, vol. 121, no. 121, pp. 279--290, Feb. 2006.
[28]
S. Zhang and O. Bodenreider, "Experience in Aligning Anatomical Ontologies," Int. J. Semant. WebInf. Syst., vol. 3, no. 2, pp. 1--26, Feb. 2007.
[29]
S. Kotitsas, D. Pappas, I. Androutsopoulos, R. McDonald, and M. Apidianaki, "Embedding Biomedical Ontologies by
[30]
Jointly Encoding Network Structure and Textual Node Descriptors," presented at 18th BioNLP Workshop and Shared Task, Florence, Italy, Aug. 1, 2019
[31]
S. Pyysalo, F. Ginter, H. Moen, T. Salakoski, and S. Ananiadou, "Distributional semantics resources for biomedical text processing," in Proc. LBM, 2013, pp. 39--44.
[32]
J. Wieting, M. Bansal, K. Gimpel, and K. Livescu, "From Paraphrase Database to Compositional Paraphrase Model and Back," TACL, vol. 3, pp. 345--358, Jun. 2015.
Index Terms
- Ontology Matching by Jointly Encoding Terminological Description and Network Structure
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September 2020
93 pages
ISBN:9781450375276
DOI:10.1145/3429523
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Published: 09 November 2020
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CCIOT 2020
CCIOT 2020: 2020 5th International Conference on Cloud Computing and Internet of Things
September 22 - 24, 2020
Okinawa, Japan
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