Both conceptual modeling and machine learning have long been recognized as important
areas of research. With the increasing emphasis on digitizing and processing large amounts …

In order to achieve interoperability of information in the context of the Semantic Web, it is
necessary to find effective ways to align different ontologies. As the number of ontologies …

Ontology matching is a task that has attracted considerable attention in recent years. With
very few exceptions, however, research in ontology matching has focused primarily on the …

Background and objective Healthcare systems deal with multiple challenges in releasing
information from data silos, finding it almost impossible to be implemented, maintained and …

Ontology Matching (OM) is performed to find semantic correspondences between the entity
elements of different ontologies to enable semantic integration, reuse, and interoperability …

The volume of research articles published today associated to instance-based ontology
matching is significant and it is thought to reflect the growing interest of ontology matching …

A common solution to the semantic heterogeneity problem is to perform knowledge graph
(KG) extension exploiting the information encoded in one or more candidate KGs, where the …

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 …

The ontology matching is an active field of research, which is considered as a key solution to
solve the semantic heterogeneity problem. Given two ontologies, the alignment process …

Background We are currently facing a proliferation of heterogeneous biomedical data
sources accessible through various knowledge-based applications. These data are …