Main research areas spanned in our investigation

Machine Learning & Inductive Reasoning

Machine Learning methods and systems based on symbolic and numerical approaches. Integration in different fragments of first order logic for the development of reasoning techniques under uncertainty.
Inductive Logic Programming: Incremental and Multistrategy Learning: integration of induction, abduction, abstraction
Inductive methods for ontologies and Semantic Web representations (founded in description logics). Similarity-based methods: distance and (dis)similarity measures: Nearest Neighbor, Kernels for structured representations.
Inductive frameworks for hybrid representation systems: DL+LP.


Data Mining & Knowledge Discovery in Databases

Analysis and synthesis of algorithms for inductive inference: classification and regression trees, causal inference, and induction of logic theories from numeric/symbolic data. Knowledge discovery in databases. Mining of Association rules.


Knowledge Management & Personalization

Enterprise Content Management: RDF Knowledge Bases. Recommender Systems
Information Retrieval: Text Mining and Categorization
Web-based applications. Personalization: user Profiling and recommendation.