Learning and aligning ontologies: methodologies and algorithms


Amphithéatre F107, INRIA Grenoble Rhône-Alpes, Montbonnot

Kate Cerqueira Revoredo and Fernanda Baião - Federal University of the State of Rio de Janeiro (UNIRIO)

Kate Cerqueira Revoredo is a Professor at Applied Informatics Department of the Federal University of the State of Rio de Janeiro, Brazil. She obtained her M.Sc and D.Sc. degree in Computer Science from Federal University of Rio de Janeiro (COPPE-UFRJ). During the year 2006 she worked for six months at the University of Freiburg, Freiburg (Germany) as part of her D.Sc. research. Her experience and research work focus on machine learning, data mining, social media analytics and ontology alignment. She participates in several program committees of national and international jornals and conferences, and is a member of the Brazilian Computer Society and the Special Comission in Artificial Intelligence.
Fernanda Baião is an Associate Professor of the Department of Applied Informatics at the Federal University of the State of Rio de Janeiro (UNIRIO) since 2004. She worked at the University of Wisconsin, Madison (USA) as a visiting student in 2000. Her current research interests include conceptual modelling, well-founded representation languages, information architecture, data management in distributed and parallel environments, and scientific workflows. Since 2009 she has been one of the research members of the Brazilian WebScience Research Institute. She participates in research projects funded by brazilian and international agencies.

Entrée libre. http://exmo.inria.fr/seminars/2014revoredo.html

Typically, an ontology formalizes a number of inter-related concepts in a domain of discourse. It represents a crucial artifact for several applications, such as semantic data integration, systems interoperability and knowledge management. However, since manually defining such ontologies is a complex, time consuming and error-prone task, there are a number of research efforts towards (semi)automatically learning ontologies. Furthermore, concepts and relationships of the domain of discourse evolve over time, thus the corresponding ontology must evolve as well and automatic mechanisms are also demanded to improve the ontology evolution process. From another perspective, since several ontologies typically exist representing the same domain, there is a need of establishing an alignment among them. Ontology Matching is a very active research area that essentially aims to automatically establish correspondences between entities of two ontologies. Despite the research efforts in these three areas, there are still interesting challenges to be solved. In this talk we will discuss approaches that are being developed in our research group for automatically learning and revising an ontology using theory revision techniques, as well as ontology matching approaches that consider user feedback within an active-learning technique and the development and reuse of correspondence antipatterns.