ABSTRACT Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot handle uncertain knowledge about an application field, which is unsuitable for the medical domain. In this paper, we focus on modeling incomplete knowledge in the classical OWL ontologies, using Bayesian networks, all keeping the semantic of the first ontology, and applying algorithms dedicated to learn parameters of Bayesian networks in order to generate the Bayesian networks. We use EM algorithm for learning conditional probability tables of different nodes of Bayesian network automatically, contrary to different tools of Bayesian networks where probabilities are inserted manually. To validate our work, we have applied our model on the diagnosis of liver cancer using classical ontology containing incomplete instances, in order to handle medical uncertain knowledge, for predicting a liver cancer.

中文翻译：

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使用贝叶斯网络对语义网的不完全知识建模

摘要 生物医学领域已经存在可互操作的本体，使科学家能够以最小的歧义进行交流。不幸的是，语义网络中的本体语言，如 OWL 和 RDF(S)，基于清晰的逻辑，因此它们无法处理关于应用领域的不确定知识，这不适用于医学领域。在本文中，我们专注于对经典 OWL 本体中的不完整知识进行建模，使用贝叶斯网络，保持第一个本体的语义，并应用专门学习贝叶斯网络参数的算法以生成贝叶斯网络。我们使用EM算法自动学习贝叶斯网络不同节点的条件概率表，这与手动插入概率的贝叶斯网络不同工具相反。