本体比较的方法论方法:SLAM本体的建议与应用

Yudith Cardinale, M. Cornejo-Lupa, Regina P. Ticona-Herrera, D. Barrios-Aranibar
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引用次数: 2

摘要

使用灵活且定义良好的模型(如本体)表示与任何领域相关的知识,为开发高效且可互操作的解决方案提供了基础。因此,在许多领域中释放了大量的本体。有必要定义如何比较这些本体,以确定哪一个最适合用户/开发人员的特定需求。自本体出现以来,一些研究提出了评价本体的标准。然而,仍然缺乏实用的和可重复的指导方针来推动本体的比较评价作为一个系统的过程。在本文中,我们提出了一种方法学过程,从词汇、结构和领域知识层面对本体进行定性和定量比较,同时考虑到正确性和质量的观点。由于我们的提案的评估方法基于黄金标准,因此可以自定义它以比较任何领域中的本体。为了证明我们的建议的适用性,我们应用我们的方法对机器人领域的本体进行了比较研究,特别是针对同时定位和映射(SLAM)问题。通过本研究案例,我们证明了通过这种方法比较过程,我们能够识别本体论的优点和缺点,以及目标领域中仍然需要填补的空白(我们的研究案例的SLAM)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Methodological Approach to Compare Ontologies: Proposal and Application for SLAM Ontologies
Representation of the knowledge related to any domain with flexible and well-defined models, such as ontologies, provides the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. It is necessary to define how to compare such ontologies to decide which one is the most suitable for specific needs of users/developers. Since the emerging developing of ontologies, several studies have proposed criteria to evaluate them. Nevertheless, there is still a lack of practical and reproducible guidelines to drive a comparative evaluation of ontologies as a systematic process. In this paper, we propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural, and Domain Knowledge levels, considering Correctness and Quality perspectives. Since the evaluation methods of our proposal are based in a golden-standard, it can be customized to compare ontologies in any domain. To show the suitability of our proposal, we apply our methodological approach to conduct a comparative study of ontologies in the robotic domain, in particularly for the Simultaneous Localization and Mapping (SLAM) problem. With this study case, we demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domain (SLAM for our study case).
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