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Ontology Reasoning Using Rules in an eHealth Context

机译:在eHealth上下文中使用规则进行本体推理

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Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals.
机译:传统上,医院的护士呼叫系统非常简单:患者在床旁有一个按钮可以呼叫护士。由于没有其他可用信息,因此无法控制要呼叫哪个特定的护士。这对于基于语义知识的解决方案来说是不同的:如果知道护理人员的状态(忙碌或忙碌),他们的当前位置以及例如他们的技能,系统总是可以选择最适合的护士来接听电话。在本文中,我们描述了使用EYE推理程序和Notation3规则实现的语义护士呼叫系统。该系统能够执行OWL-RL推理。此外,我们使用规则来实现复杂的决策树。我们将解决方案与使用OWL-DL,Pellet推理程序和SPARQL查询的实现进行比较。我们证明了,我们纯粹基于规则的方法给出了可喜的结果。进一步的改进将产生成熟的产品,这将显着改变现代医院的组织。

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