We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain experts.

中文翻译：

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本体网络中的人类活动识别模型

我们提出了Arianna +，这是一个设计本体网络的框架，用于表示知识，使智能家居能够在线执行人类活动识别。在网络中，节点是允许各种数据上下文关联的本体，而边缘是精心设计数据的通用计算过程。Arianna +在过程和语句的输入和输出之间提供灵活的接口，这些过程和语句是本体论知识的原子表示。Arianna +通过采用基于逻辑的推理，即通过检查本体中某些语句的分类，基于事件来调度过程。每个过程都涉及输入和输出语句，这些输入和输出语句根据特定的先验知识在本体中的上下文不同。Arianna +允许设计在多个上下文中对数据进行编码的网络，作为参考方案，我们提出了一个基于所有活动之间共享的空间上下文和专门针对每个活动被识别的时间上下文的模块化网络。在本文中，我们认为，与编码相同知识的单个本体相比，小型本体的网络更易于理解，并且计算负荷更低。Arianna +在同一体系结构中集成了异构数据处理技术，这可能更适合于不同的环境。因此，我们没有提出用于活动识别的新算法，而是将重点放在体系结构方面，以便以面向上下文的方式适应基于逻辑和数据驱动的活动模型。还，