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. We also show that our approach is
even effective when training data from another domain (transfer learning) is used.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2019-105471RB-I00Junta de Andalucía P18-RT-106
