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Performance Evaluation of Upper-Level Ontologies in Developing Materials Science Ontologies and Knowledge Graphs

Advanced Engineering Materials(2024)SCI 3区

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Abstract
This study tackles a significant challenge in ontology development for materials science: selecting the most appropriate upper-level ontologies for creating application-level ontologies and knowledge graphs. Focusing on the use case of Brinell hardness testing, the research assesses the performance of various top-level ontologies (TLOs)-basic formal ontology (BFO), elementary multiperspective material ontology (EMMO), and provenance ontology (PROVO)-in developing Brinell testing ontologies (BTOs). Consequently, three versions of BTOs are created using combinations of these TLOs along with their integrated mid- and domain-level ontologies. The performance of these ontologies is evaluated based on ten parameters: semantic richness, domain coverage, extensibility, complexity, mapping efficiency, query efficiency, integration with other ontologies, adaptability to different data contexts, community acceptance, and documentation and maintainability. The results show that all candidate TLOs can effectively develop BTOs, each with its distinct advantages. BFO provides a well-structured, understandable hierarchy, and excellent query efficiency, making it suitable for integration across various ontologies and applications. PROVO demonstrates balanced performance with strong integration capabilities. Meanwhile, EMMO offers high semantic richness and domain coverage, though its complex structure impacts query efficiency and integration with other ontologies.
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Key words
Brinell hardness,knowledge graph,materials science,ontology evaluation,top-level ontology
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要点】:本研究评估了在材料科学领域开发应用级本体和知识图谱时,不同顶层本体的性能,以确定最合适的本体选择,并以布氏硬度测试为案例进行验证。

方法】:研究通过比较基本形式本体(BFO)、基本多视角材料本体(EMMO)和起源本体(PROVO)的性能,创建了三个版本的布氏测试本体(BTO),并基于十个参数进行评估。

实验】:实验通过构建和使用BFO、EMMO和PROVO结合其中间和领域级本体的BTO,评估了这些本体的性能,数据集名称未明确提及,结果展示BFO、PROVO和EMMO各有优势。