OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies

OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity. If the components of an architecture also exploit OWL ontologies for knowledge representation and reasoning, they would require to be interfaced with OWL axioms. Since OWL does not adhere to the OOP paradigm, such an interface often leads to boilerplate code affecting modularity, and OWLOOP is designed to address this issue as well as the associated computational aspects. We present an extension of the OWL-API to provide a general-purpose interface between OWL axioms subject to reasoning and modular OOP objects hierarchies.

Human Activity Recognition Models in Ontology Networks

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.

Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies

Aging population ratios are rising significantly. Meanwhile, smart home based health monitoring services are evolving rapidly to become a viable alternative to traditional healthcare solutions. Such services can augment qualitative analyses done by gerontologists with quantitative data. Hence, the recognition of Activities of Daily Living (ADL) has become an active domain of research in recent times. For a system to perform human activity recognition in a real-world environment, multiple requirements exist, such as scalability, robustness, ability to deal with uncertainty (e.g., missing sensor data), to operate with multi-occupants and to take into account their privacy and security. This paper attempts to address the requirements of scalability and robustness, by describing a reasoning mechanism based on modular spatial and/or temporal context models as a network of ontologies. The reasoning mechanism has been implemented in a smart home system referred to as Arianna+. The paper presents and discusses a use case, and experiments are performed on a simulated dataset, to showcase Arianna+'s modularity feature, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.

Towards a new paradigm for assistive technology at home: research challenges, design issues and performance assessment

Providing elderly and people with special needs, including those suffering from physical disabilities and chronic diseases, with the possibility of retaining their independence at best is one of the most important challenges our society is expected to face. Assistance models based on the home care paradigm are being adopted rapidly in almost all industrialized and emerging countries. Such paradigms hypothesize that it is necessary to ensure that the so-called Activities of Daily Living are correctly and regularly performed by the assisted person to increase the perception of an improved quality of life. This chapter describes the computational inference engine at the core of Arianna, a system able to understand whether an assisted person performs a given set of ADL and to motivate him/her in performing them through a speech-mediated motivational dialogue, using a set of nearables to be installed in an apartment, plus a wearable to be worn or fit in garments.

Scene learning, recognition and similarity detection in a fuzzy ontology via human examples

This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects; (ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios.

A ROS multi-ontology references services: OWL reasoners and application prototyping issues

The challenge of sharing and communicating information is crucial in complex human-robot interaction (HRI) scenarios. Ontologies and symbolic reasoning are the state-of-the-art approaches for a natural representation of knowledge, especially within the Semantic Web domain. In such a context, scripted paradigms have been adopted to achieve high expressiveness. Nevertheless, since symbolic reasoning is a high complexity problem, optimizing its performance requires a careful design of the knowledge. Specifically, a robot architecture requires the integration of several components implementing different behaviors and generating a series of beliefs. Most of the components are expected to access, manipulate, and reason upon a run-time generated semantic representation of knowledge grounding robot behaviors and perceptions through formal axioms, with soft real-time requirements.