In this paper we present an ontology-driven framework for natural language question analysis and answering over user models (e.g. preferences, habits and health problems of individuals) that are formally captured using ontology design patterns. Pattern-based modelling is extremely useful for capturing n-ary relations in a well-defi ned and axiomatised manner, but it introduces additional challenges in building NL interfaces for accessing the underlying content. This is mainly due to the encapsulation of domain semantics inside conceptual layers of abstraction (e.g. using reification or container classes) that demand flexible, context-aware approaches for query analysis and interpretation. We describe the coupling of a frame-based formalisation of natural language user utterances with a context-aware query interpretation towards question answering over pattern-based RDF knowledge bases. The proposed framework is part of a human-like socially communicative agent that acts as an intermediate between elderly migrants and care personnel, assisting the latter to solicit personal information about care recipients (e.g. medical history, care needs, preferences, routines, habits, etc.).
Dr. Georgios Meditskos received his PhD degree in Informatics from Aristotle University of Thessaloniki in Greece for his dissertation on "Semantic Web Service Discovery and Ontology Reasoning using Entailment Rules". He also holds an MSc and a BSc degree from the same department. Since January 2012 he is working as a postdoctoral research fellow at the Information Technologies Institute (ITI) of the Center for Research and Technology Hellas (CERTH). He has participated in numerous European and national research projects and he is the author of more than 45 publications in refereed journals and international conferences. His research interests include Knowledge Representation and Reasoning in the Semantic Web (RDF/OWL, rule-based ontology reasoning, combination of rules and ontologies), Semantic Web Services (discovery, composition) and Context-based multi-sensor reasoning and fusion in Pervasive Environments. Recently, he has been actively involved in the development of semantic interpretation frameworks for the high-level integration, analysis and preservation of heterogeneous contextual information in the healthcare domain
