Thanos G. Stavropoulos

User-Driven Ontology Population from Linked Data Sources

In order for ontology-based applications to be deployed in real-life scenarios, significant volumes of data are required to populate the underlying models. Populating ontologies manually is a time-consuming and error-prone task and, thus, research has shifted its attention to automatic ontology population methodologies. However, the majority of the proposed approaches and tools focus on analysing natural language text and often neglect other more appropriate sources of information, such as the already structured and semantically rich sets of Linked Data. The paper presents PROPheT, a novel ontology population tool for retrieving instances from Linked Data sources and subsequently inserting them into an OWL ontology. The tool, to the best of our knowledge, offers entirely novel ontology population functionality to a great extent and has already been positively received according to user evaluation.

A Sensor-Based Framework to Support Clinicians in Dementia Assessment: The Results of a Pilot Study

This paper presents the main mechanisms of a sensor-based framework to support clinical diagnosis of people suffering from Alzheimer disease and dementia. The framework monitors patients at a lab environment while trying to accomplish specific tasks. Different types of sensors are used for monitoring the patients, while a graphical user interface enables the clinicians to access and visualize the results. Sensor data is semantically integrated and analyzed using knowledge-driven interpretation techniques based on Semantic Web technologies. Moreover, this paper presents encouraging preliminary results of a pilot study in which 59 patients (29 Alzheimer disease –AD– and 30 mild cognitive impairment –MCI) participated in a clinical protocol. Their analysis indicated that MCI patients outperformed AD patients in specific tasks of the protocol, verifying the initial clinical assessment.

The SemaDrift Protégé Plugin to Measure Semantic Drift in Ontologies: Lessons Learned

Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents the findings, current limitations and lessons learned throughout the development and the application of a novel software tool, developed in the context of the PERICLES FP7 project, which integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protege. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.

Supporting Cognitive Skills of People Suffering from Dementia through a Sensor-Based System

Cognitive Rehabilitation aims to enable people with cognitive impairments to optimize their cognitive functioning in the everyday context, in order to prevent or reduce excess disability, and thus improve quality of life and well-being. This paper presents the main elements of a sensor-based system to support cognitive skills of people suffering from Alzheimer disease (AD) and dementia. The system monitors the patients at their homes during the performance of daily activities. Different types of sensors are used for monitoring the patients, while a graphical user interface a) enables the clinicians to access and visualize the results and b) supports the patients through specific interventions. The sensor data is semantically integrated and analyzed using knowledge-driven interpretation techniques based on Semantic Web technologies. Overall, this paper presents a) the system, b) cognitive rehabilitation scenarios and c) the relevant evaluation methods.

Intelligent User Interfaces to Support Diagnosis and Assessment of People with Dementia: An Expert Evaluation

This paper presents the main user/clinician interface and the mechanisms of a sensors-based system to support clinicians’ diagnosis for people suffering from Alzheimer disease and dementia. The system monitors the patient at a lab or a home environment when he/she tries to accomplish specific tasks or ordinary daily activities. The main goal of the system is to support both the clinical assessment and therapy. The system can be divided into two main parts: (a) the sensors, which monitor the patients and (b) the clinician user interface, which includes the main system operation as well as the results of the monitoring. The data between these two parts is transferred and interpreted by using knowledge-driven interpretation techniques based on Semantic Web technologies. In order to evaluate the interface satisfaction, the usefulness and the ease of use of the clinician interface both for the lab and home environments, an expert evaluation was conducted with 2 groups of professionally active psychologists with dementia expertise (14 psychologists for the lab and 10 for the home environment). The results of the questionnaire-based evaluation showed that the clinicians are quite positive about the use of the system as a supporting method to dementia assessment and therapy.