ONTOCONNECT: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling

Jaydeep Chakraborty, Hamada M. Zahera, Mohamed Ahmed Sherif, Srividya K. Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
EditorsM. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages942-945
Number of pages4
ISBN (Electronic)9781665443371
DOIs
StatePublished - 2021
Event20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States
Duration: Dec 13 2021Dec 16 2021

Publication series

NameProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/13/2112/16/21

Keywords

  • Graph embedding with negative sampling
  • Graph Neural Network
  • Ontology Schema Alignment

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications

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