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In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster. We conducted experiments with this approach on the real world application of aligning knowledge in the field of pharmacogenomics, which motivated our study. We particularly investigated the interplay between domain knowledge and GCN models with the two following focuses. First, we applied inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Second, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the \u201cstrength\u201d of these different relations (e.g., smaller distances for equivalences), letting us considering clustering and distances in the embedding space as a means to suggest alignment relations in our case study.<\/jats:p>","DOI":"10.3233\/sw-210452","type":"journal-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T18:40:35Z","timestamp":1637692835000},"page":"379-398","source":"Crossref","is-referenced-by-count":2,"title":["Discovering alignment relations with Graph Convolutional Networks: A biomedical case study"],"prefix":"10.1177","volume":"13","author":[{"given":"Pierre","family":"Monnin","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"},{"name":"Orange, Belfort, France"}]},{"given":"Chedy","family":"Ra\u00efssi","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"},{"name":"Ubisoft, Singapore"}]},{"given":"Amedeo","family":"Napoli","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"}]},{"given":"Adrien","family":"Coulet","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France"},{"name":"Inria Paris, F-75012 Paris, France"},{"name":"Centre de Recherche des Cordeliers (UMR1138 Inserm, Universit\u00e9 de Paris, Sorbonne Universit\u00e9), F-75006 Paris, France"}]}],"member":"179","reference":[{"key":"10.3233\/SW-210452_ref1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304187"},{"key":"10.3233\/SW-210452_ref2","unstructured":"F.\u00a0Baader et al.\u00a0(eds), The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2003."},{"issue":"5","key":"10.3233\/SW-210452_ref3","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1038\/scientificamerican0501-34","article-title":"The semantic web","volume":"284","author":"Berners-Lee","year":"2001","journal-title":"Scientific American"},{"key":"10.3233\/SW-210452_ref4","unstructured":"A.\u00a0Bordes, N.\u00a0Usunier, A.\u00a0Garc\u00eda-Dur\u00e1n, J.\u00a0Weston and O.\u00a0Yakhnenko, Translating embeddings for modeling multi-relational data, in: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, Proceedings of a meeting held December 5\u20138, 2013, Lake Tahoe, Nevada, United States, 2013, pp.\u00a02787\u20132795."},{"issue":"9","key":"10.3233\/SW-210452_ref5","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Trans. 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