Using Ontologies to Enhance Data Management in Distributed Environments Carlos Eduardo Pires 1 , Damires Souza 2 , Bernadette Lóscio 3 , Rosalie Belian 4 , Patricia Tedesco 3 and Ana Carolina Salgado 3 1 Federal University of Campina Grande (UFCG), Computer Science Department Av. Aprígio Veloso, 882, Bodocongó - 58109-970 - Campina Grande, PB, Brazil cesp@dsc.ufcg.edu.br 2 Federal Institute of Education, Science and Technology of Paraiba (IFPB), Brazil Av. Primeiro de Maio, 720, Jaguaribe - 58015-430 - João Pessoa - Paraíba damires@ifpb.edu.br 3 Federal University of Pernambuco (UFPE), Center for Informatics Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife, PE, Brazil {bfl, pcart, acs}@cin.ufpe.br 4 Federal University of Pernambuco (UFPE), Center of Health Sciences Av. Prof. Moraes Rego, S/N, Cidade Universitária – 50670-901 – Recife, PE, Brazil rosalie.belian@ufpe.br Abstract. Data management solutions in distributed environments have been continuously evolving during the last years to answer users’ needs and face new technology challenges. To help matters, ontologies have been used as a support for the techniques of managing data. For instance, ontologies may be used to describe the semantics of data at different sources, helping to overcome problems of semantic interoperability and data heterogeneity, and thus assisting schema integration and query answering over the distributed data sources. The goal of this paper is to highlight the use of ontologies in order to enhance data management issues in distributed environments. To this end, we describe a set of ongoing works which have been developed in our research. Keywords: Ontology, Semantics, Data Management, Distributed Environment. 1 Introduction and Research Statement The increasing use of computers and the development of communication infrastructures have led to a demand for high-level integration of autonomous and heterogeneous data sources. This fact caused the development of diverse distributed environments, including Data Integration Systems [Halevy et al. 2006], Peer Data Management Systems (PDMSs) [Sung et al. 2005], and Dataspaces [Hedeler et al. 2009]. While these types of data integration systems differ with respect to their level of coupling, all of them have in common the need of dealing with heterogeneity, mappings, and query answering. Particularly, these dynamic distributed environments are characterized by an architecture constituted by various autonomous data sources (e.g., websites, files, databases), here referred to as peers. These peers are linked to