A Novel Accurate and Time Efficient Map Reduce Approach for Biomedical Ontology Alignment

This research focuses on enhancing ontology mapping in the biomedical domain by reducing execution time and automating the mapping process. Biomedical ontologies are crucial for accurate interpretation of medical records and decision making. However, two main challenges are overlapping concepts and a large search space. To address these issues, a distributed environment using the Hadoop framework is implemented, and a Map-Reduce algorithm is employed for parallelizing the mapping system, resulting in significant time reduction. For precise alignment, an Extreme Learning Machine based neural network is utilized. Evaluation is conducted using OAEI and OBO biomedical ontologies, with simulation results demonstrating notable improvements in execution time and evaluation metrics using the proposed ontology mapping system's multi-strategy similarity metrics. The mapping between ontologies is represented using the Alignment API, simplifying the calculation process.

Automated summary

This research aims to improve the efficiency and automation of ontology mapping in the biomedical domain. Biomedical ontologies are crucial for accurate interpretation of medical records and decision making, but two main challenges are overlapping concepts and a large search space. To address these issues, a distributed environment using the Hadoop framework is implemented, and a Map-Reduce algorithm is employed for parallelizing the mapping system, resulting in significant time reduction. An Extreme Learning Machine based neural network is utilized for precise alignment. Evaluation using OAEI and OBO biomedical ontologies demonstrates notable improvements in execution time and evaluation metrics with the proposed multi-strategy similarity metrics of the ontology mapping system. The mapping between ontologies is represented using the Alignment API, simplifying the calculation process.