Natural Interaction and Knowledge Management Technologies

Our main research and development themes are the following:

Semantic Technologies
The research activity is focused on the study of data models and patterns for realizing semantics aware applications and services. It covers several semantic aspects, such as Natural Language Processing (NLP) and Text Categorization, formalization of specific knowledge domains through ontologies as necessary components of semantic-based systems. Main focus is on semantic relations built to be processed automatically. In particular we study aspects related to the semantic indexing and the automatic categorization of web resources and develop tools and algorithms based on NLP in order to extract the meaning expressed in textual resources, by applying a process including a syntactic and a semantic analysis able to resolve relations and lexical ambiguity inherent in natural languages.

Profiling Systems
The research activity is focused on the study and testing of innovative methodologies aimed at developing personalized solutions which, through the analysis of the user behavior, are able to understand his interests and preferences in different areas of use, and are able to simplify the human machine interacion. The profiling systems, implementing the storing, the selection and the analysis of personal data, and working with recommendation methodologies based on collaborative filtering algorithms, allow the machine to know the person whom is interacting with and to establish a network of links between users which provides accesses to information more efficient.

Human Computer Interaction
Our research in this field is aimed at designing and evaluating novel interaction techniques suitable for working and playing in technology enhanced places. Beginning with graphical user interfaces, up to the latest gesture based and multitouch interfaces, the computer is evolving to encompass more and more natural abilities in the interaction. Manipulative interfaces help users to solve complex problems, foster efficiency, are easier to learn and to remember, since they are based on natural experience. Our goal is to let the computer sense and understand gestures and natural behaviors, thus making it able (or closer) to catch the richness of human expression.

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