Ecology and Biodiversity Ontology Alignment for Smart Environment via Adaptive Compact Evolutionary Algorithm

Smart Environment (SE) focuses on the initiatives for healthy living, where ecological issues and biodiversity play a vital role in the environment and sustainability. To manage the knowledge on ecology and biodiversity and preserve the ecosystem and biodiversity simultaneously, it is necessary to align the data entities in different ecology and biodiversity ontologies. Since the problem of Ecology and Biodiversity Ontology Alignment (EBOA) is a large-scale optimization problem with sparse solutions, finding high-quality EBOA is an open challenge. Evolutionary Algorithm (EA) is a state-of-the-art technique in the ontology aligning domain, and this study further proposes an Adaptive Compact EA (ACEA) to address the problem of EBOA, which uses semantic reasoning to reduce searching space and adaptively guides searching direction to improve the algorithm's performance. In addition, we formally model the problem of EBOA as a discrete optimization problem, which maximizes the alignment's completeness and correctness through determining an optimal entity corresponding set. After that, a hybrid entity similarity measure is presented to distinguish the heterogeneous data entities, and an ACEA-based aligning technique is proposed. The experiment uses the famous Biodiversity and Ecology track to test ACEA's performance, and the experimental results show that ACEA-based aligning technique statistically outperforms other EA-based and state-of-the-art aligning techniques.

Automated summary

This study proposes an Adaptive Compact Evolutionary Algorithm (ACEA) to address the problem of aligning ecology and biodiversity ontologies. By utilizing semantic reasoning and optimization techniques, the algorithm improves performance and achieves better results compared to other aligning techniques in experiments.