As a unified model for describing biomedical knowledge, a biomedical ontology is of help to
solve the issues of data heterogeneity in different biomedical databases. However, these …

Ontology matching is an efficient method to establish interoperability among heterogeneous
ontologies. Large-scale ontology matching still remains a big challenge for its long time and …

Large ontologies still pose serious challenges to state-of-the-art ontology alignment
systems. In this paper we present an approach that combines a neural embedding model …

Biomedical ontology offers a structured framework to model the biomedical knowledge in a
machine-readable format. However, the heterogeneity inherent in biomedical ontologies …

POMap++ is a novel ontology matching system based on a machine learning approach. This
year is the second participation of POMap++ in the Ontology Alignment Evaluation Initiative …

AgreementMakerDeep (AMD) is a new flexible and extensible ontology matching system. It
exploits the contextual and structural information of ontologies by infusing knowledge to pre …

AgreementMakerDeep (AMD) is a new flexible and extensible ontology matching system
with knowledge graph embedding techniques. AMD learns from classes and their relations …

Matching learning corresponds to the combination of ontology matching and machine
learning techniques. This strategy has gained increasing attention in recent years. However …

The growing usage of Semantic Web has resulted in an increasing number, size and
heterogeneity of ontologies on the web. Therefore, the necessity of ontology matching …

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from
knowledge graphs (eg, Wikidata or DB-pedia) to the elements of a table. This task is a …