  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.
