{"type":"ScholarlyArticle","title":"Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping","authors":[{"name":"Diego De Uña","slug":"diego-de-una","given":"Diego","family":"De Uña","affiliation":"Department of Computing and Information Systems - The University of Melbourne"},{"name":"Nataliia Rümmele","slug":"nataliia-rummele","given":"Nataliia","family":"Rümmele","affiliation":"Siemens , Germany"},{"name":"Graeme Gange","slug":"graeme-gange","given":"Graeme","family":"Gange","affiliation":"Department of Computing and Information Systems - The University of Melbourne"},{"name":"Peter Schachte","slug":"peter-schachte","given":"Peter","family":"Schachte","affiliation":"Department of Computing and Information Systems - The University of Melbourne"},{"name":"Peter J. Stuckey","slug":"peter-j-stuckey","given":"Peter J.","family":"Stuckey","affiliation":"Department of Computing and Information Systems - The University of Melbourne; Data61 , CSIRO , Melbourne , Australia"}],"abstract":"The problem of integrating heterogeneous data sources into an ontology is highly relevant in the database field.\r\nSeveral techniques exist to approach the problem, but side constraints on the data cannot be easily implemented and thus the results may be inconsistent.\r\nIn this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP).\r\nCombining ML and CP achieves state-of-the-art precision, recall and speed, and provides a more flexible framework for variations of the problem.","doi":"10.24963/ijcai.2018/178","url":"https://ijcai.org/proceedings/2018/178","pdf":"https://ijcai.org/proceedings/2018/0178.pdf","markdown":"https://ijcai.org/proceedings/2018/178.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/178","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Constraints and SAT","track":"Main track","number":178,"pages":{"first":1277,"last":1283},"keywords":["Multidisciplinary Topics and Applications: Intelligent Database Systems","Constraints and SAT: Modeling;Formulation","Constraints and SAT: Constraints and Data Mining ; Machine Learning","Knowledge Representation and Reasoning: Information Fusion"],"videos":[],"references":[{"position":1,"raw":"[Chu, 2011] G.G. 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