Print Email Facebook Twitter An exploratory journey to combine schema matchers for better relevance prediction Title An exploratory journey to combine schema matchers for better relevance prediction Author Wang, Wang Hao (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Katsifodimos, A (mentor) Houben, G.J.P.M. (graduation committee) Chen, Lydia Y. (graduation committee) Ionescu, A. (mentor) Degree granting institution Delft University of Technology Programme Computer Science | Software Technology Date 2022-12-01 Abstract Current speed of data growth has exponentially increased over the past decade, highlighting the need of modern organizations for data discovery systems. Several (automated) schema matching approaches have been proposed to find related data, exploiting different parts of schema information (e.g. data type, data distribution, column name, etc.). However, research showed that single schema matching techniques fails to effectively match schemas, whilst combinatorial schema matching systems show more promise. With the introduction of combinatorial schema matching systems, new challenges arise regarding selection and combining strategies. This research attempts to explore different techniques for determining the importance of each matcher in a combinatorial schema matching system by determining the weights of each matcher and comparing them through a comprehensive evaluation. Subject Schema MatchingData DiscoveryCombining Schema Matchers To reference this document use: http://resolver.tudelft.nl/uuid:fba2c5a7-8769-4ee8-90e2-7d361fc41c03 Part of collection Student theses Document type master thesis Rights © 2022 Wang Hao Wang Files PDF Thesis_Wang_Hao_Wang.pdf 3.82 MB Close viewer /islandora/object/uuid:fba2c5a7-8769-4ee8-90e2-7d361fc41c03/datastream/OBJ/view