ONTOCONNECT: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling
Jaydeep Chakraborty, Hamada M. Zahera, Mohamed Ahmed Sherif, Srividya Kona Bansal · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · 2021
The ontology alignment task aims at linking two or more different ontologies from the same domain or different domains. Over the years, many techniques have been proposed for ontology instance alignment, schema alignment, and link discovery. Most of the available approaches require human intervention or work within a specific domain and follow a rule-based and logic-based approach. In this paper, we present an ontology alignment approach using graph embedding with negative sampling that is independent of the domain and does not require any human intervention.