Ponencia
Towards the smart use of embedding and instance features for property matching
Autor/es | Ayala Hernández, Daniel
Hernández Salmerón, Inmaculada Concepción Ruiz Cortés, David Rahm, Erhard |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2021 |
Fecha de depósito | 2021-11-05 |
Publicado en |
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ISBN/ISSN | 978-1-7281-9184-3 2375-026X |
Resumen | Data integration tasks such as the creation and
extension of knowledge graphs involve the fusion of heterogeneous
entities from many sources. Matching and fusion of
such entities require to also match and combine their ... Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España Ministerio de Ciencia, Innovación y Universidades (MICINN). España Junta de Andalucía |
Identificador del proyecto | TIN2016-75394-R
PID2019-105471RB-I00 P18-RT-1060 |
Cita | Ayala Hernández, D., Hernández Salmerón, I.C., Ruiz Cortés, D. y Rahm, E. (2021). Towards the smart use of embedding and instance features for property matching. En ICDE 2021: 37th International Conference on Data Engineering (2111-2116), Chania, Greece: IEEE Computer Society. |
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