dc.creator | Ayala Hernández, Daniel | es |
dc.creator | Hernández Salmerón, Inmaculada Concepción | es |
dc.creator | Ruiz Cortés, David | es |
dc.creator | Rahm, Erhard | es |
dc.date.accessioned | 2021-02-17T10:42:26Z | |
dc.date.available | 2021-02-17T10:42:26Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Ayala Hernández, D., Hernández Salmerón, I.C., Ruiz Cortés, D. y Rahm, E. (2020). LEAPME: Learning-based Property Matching with Embeddings. ArXiv.org, arXiv:2010.01951 | |
dc.identifier.uri | https://hdl.handle.net/11441/105071 | |
dc.description.abstract | 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-speci c 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 ve baselines for several multi-source datasets with
real-world data shows the high e ectiveness of LEAPME. We also show that our approach
is even e ective when training data from another domain (transfer learning) is used. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2016-75394- R | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades PID2019-105471RB-I00 | es |
dc.description.sponsorship | Junta de Andalucía P18-RT-1060 | es |
dc.format | application/pdf | es |
dc.format.extent | 24 | es |
dc.language.iso | eng | es |
dc.publisher | Cornell University | es |
dc.relation.ispartof | ArXiv.org, arXiv:2010.01951 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data integration | es |
dc.subject | Machine learning | es |
dc.subject | Knowledge Graphs | es |
dc.title | LEAPME: Learning-based Property Matching with Embeddings | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2016-75394- R | es |
dc.relation.projectID | PID2019-105471RB-I00 | es |
dc.relation.projectID | P18-RT-1060 | es |
dc.relation.publisherversion | https://arxiv.org/abs/2010.01951 | es |
dc.journaltitle | ArXiv.org | es |
dc.publication.issue | arXiv:2010.01951 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Junta de Andalucía | es |