Xue Xingsi, Tsai Pei-Wei
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, China.
Front Plant Sci. 2022 Apr 14;13:877120. doi: 10.3389/fpls.2022.877120. eCollection 2022.
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.
智能环境（SE）专注于促进健康生活的举措，其中生态问题和生物多样性在环境与可持续性方面发挥着至关重要的作用。为了管理生态与生物多样性方面的知识并同时保护生态系统和生物多样性，有必要使不同生态与生物多样性本体中的数据实体保持一致。由于生态与生物多样性本体对齐（EBOA）问题是一个具有稀疏解的大规模优化问题，因此找到高质量的EBOA是一项公开挑战。进化算法（EA）是本体对齐领域的一种先进技术，本研究进一步提出了一种自适应紧凑进化算法（ACEA）来解决EBOA问题，该算法使用语义推理来减少搜索空间并自适应地引导搜索方向，以提高算法性能。此外，我们将EBOA问题形式化建模为一个离散优化问题，通过确定最优实体对应集来最大化对齐的完整性和正确性。之后，提出了一种混合实体相似性度量来区分异构数据实体，并提出了一种基于ACEA的对齐技术。实验使用著名的生物多样性与生态轨迹来测试ACEA的性能，实验结果表明，基于ACEA的对齐技术在统计上优于其他基于EA的和先进的对齐技术。
