@inproceedings{ab59d159933a46d58205dc1db5f4b5e4,
title = "ONTOCONNECT: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling",
abstract = "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.",
keywords = "Graph embedding with negative sampling, Graph Neural Network, Ontology Schema Alignment",
author = "Jaydeep Chakraborty and Zahera, {Hamada M.} and Sherif, {Mohamed Ahmed} and Bansal, {Srividya K.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 ; Conference date: 13-12-2021 Through 16-12-2021",
year = "2021",
doi = "10.1109/ICMLA52953.2021.00155",
language = "English (US)",
series = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "942--945",
editor = "Wani, {M. Arif} and Sethi, {Ishwar K.} and Weisong Shi and Guangzhi Qu and Raicu, {Daniela Stan} and Ruoming Jin",
booktitle = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",
}