MONDO and DO identifiers and their common names.

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information. It would be preferable if ontologies could explicitly model real-world uncertainty and incorporate it into reasoning. We present an ontology framework which is based on a seamless synthesis of description logic and probabilistic semantics. This synthesis is powered by a link between ontology assertions and random variables that allows for automated construction of a probability distribution suitable for inferencing. Furthermore, our approach defines how to represent stochastic, uncertain, or incomplete subject matter. Additionally, this paper describes how to fuse multiple conflicting ontologies into a single knowledge base that can be reasoned with using the methods of both description logic and probabilistic inferencing. This is accomplished by using probabilistic semantics to resolve conflicts between assertions, eliminating the need to delete potentially valid knowledge and perform consistency checks. In our framework, emergent inferences can be made from a fused ontology that were not present in any of the individual ontologies, producing novel insights in a given domain.

不确定信息建模是本体（Ontology）研究中的开放性难题，也是构建真正语义网（Semantic Web）的理论障碍。当前的本体通常无法对不确定性进行建模，因此涉及随机主题的内容要么必须进行归一化处理，要么被完全舍弃。由于不确定性在现实世界中无处不在，知识工程师常常陷入两难境地：要么开展人力成本极高的密集型研究，要么承担舍弃正确信息、接纳错误信息的风险。若本体能够显式地对现实世界中的不确定性进行建模，并将其融入推理过程，将是更为理想的方案。本文提出了一种基于描述逻辑（Description Logic）与概率语义学无缝融合的本体框架，该框架依托于本体断言与随机变量之间的关联，可自动构建适用于推理的概率分布。此外，本方法还定义了如何表示随机、不确定或不完整的主题内容。同时，本文还阐述了如何将多个存在冲突的本体融合为单一知识库（Knowledge Base），该知识库可同时使用描述逻辑与概率推理的方法进行推理。该过程通过概率语义学解决本体断言间的冲突得以实现，无需删除潜在有效的知识，也无需执行一致性校验。在本框架中，可从融合后的本体中生成此前任意单个本体均未包含的涌现推理结果，为特定领域带来全新的认知洞察。