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 | 2021-11-05T08:36:54Z | |
dc.date.available | 2021-11-05T08:36:54Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | 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. | |
dc.identifier.isbn | 978-1-7281-9184-3 | es |
dc.identifier.issn | 2375-026X | es |
dc.identifier.uri | https://hdl.handle.net/11441/127100 | |
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-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. | 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 | 6 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | ICDE 2021: 37th International Conference on Data Engineering (2021), pp. 2111-2116. | |
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 | Towards the smart use of embedding and instance features for property matching | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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://ieeexplore.ieee.org/document/9458651 | es |
dc.identifier.doi | 10.1109/ICDE51399.2021.00209 | es |
dc.publication.initialPage | 2111 | es |
dc.publication.endPage | 2116 | es |
dc.eventtitle | ICDE 2021: 37th International Conference on Data Engineering | es |
dc.eventinstitution | Chania, Greece | es |
dc.relation.publicationplace | New York, USA | 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 |