贝叶斯知识驱动的本体学:在不确定性和不完整性下融合语义知识的框架
Eugene Santos1, Jacob Jurmain1, Anthony Ragazzi1
1Thayer School of Engineering, Dartmouth College, Hanover, NH, United States of America.
PloS one
|March 27, 2024
概括
这项研究引入了一种新的本体学框架,将描述逻辑与概率语义相结合,以有效地模拟和推理不确定的信息,克服当前系统的局限性,以实现更强大的语义网络.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 当前的本体学难以表示不确定的或随机的信息,阻碍了真正的语义网络的发展.
- 现有的方法通常需要数据规范化或拒绝,导致信息丢失和潜在的不准确性.
- 在现实世界中无处不在的不确定性需要在本体学中进行明确的建模,以实现有效的知识工程.
研究的目的:
- 提出一种本体学框架,能够明确地建模现实世界的不确定性,并将其整合到推理过程中.
- 为了使在本体学中能够表示随机,不确定或不完整的主题.
- 开发一种方法,将多个相互冲突的本体学融合为一个统一的,一致的理性知识库.
主要方法:
- 提出了描述逻辑和概率语义的无合成.
- 在本体论断和随机变量之间建立联系,便于推断的自动化概率分布构造.
- 概率语义被用来解决在本体论融合过程中断言之间的冲突,保留潜在的有效知识.
主要成果:
- 该框架成功地代表了随机和不确定的主题.
- 使用概率语义的本体融合解决了冲突,而无需删除数据或进行广泛的一致性检查.
- 不存在于单个本体学中的新兴推断可以从融合的知识库中得出.
结论:
- 拟议的本体学框架为模拟和推理不确定性提供了一个强大的解决方案.
- 这种方法增强了知识表示能力,为更全面的语义网络应用铺平了道路.
- 融合相互矛盾的本体学并获得新的见解的能力显著推进了本体学研究领域.
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