Biomedical ontologies have gained particular relevance in the life science domain due to its
prominent role in representing knowledge in this domain. However, the existing biomedical …

Ontology matching is an integral part for establishing semantic interoperability. One of the
main challenges within the ontology matching operation is semantic heterogeneity, ie …

Background Biomedical ontologies pose several challenges to ontology matching due both
to the complexity of the biomedical domain and to the characteristics of the ontologies …

The growing volume, variety and complexity of data being collected for scientific purposes
presents challenges for data integration. For data to be truly useful, scientists need not only …

AgreementMakerLight (AML) is a system for automated ontology matching that is
characterized by its efficiency, extensibility, and ability to incorporate external knowledge. In …

Background: It is assumed that the implementation of health information technology
introduces new vulnerabilities within a complex sociotechnical health care system, but no …

AgreementMakerLight (AML) is an automated ontology matching framework based on
element-level matching and the use of external resources as background knowledge. This …

The use of external background knowledge can be beneficial for the task of matching
schemas or ontologies automatically. In this paper, we exploit six general-purpose …

Explainable artificial intelligence typically focuses on data-based explanations, lacking the
semantic context needed to produce human-centric explanations. This is especially relevant …

AgreementMakerLight (AML) is an ontology matching system designed with scalability,
extensibility and satisfiability as its primary guidelines, as well as an emphasis on the ability …