Knowledge discovery in evolving domains presents several challenges in information extraction and knowledge acquisition from heterogeneous, distributed, dynamic data sources. We define an evolving process if the process is developing, changing over time in a continuous manner. Examples of such domains include biological sciences, medical sciences, and social sciences, among others. This paper describes research in progress on a new methodology for leveraging the semantic content of ontologies to improve knowledge discovery in complex and dynamical domains. We consider in this initial stage the problem of how to acquire previous knowledge from data and then use this information in the context of ontology engineering. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models, followed by an analysis of some of the methods and on going researches in the process of building ontology from databases in evolving domains, or ontology learning from databases. In the second part we describe our approach to build a framework able to enhance ontology learning and discovery from data and present future directions of our research integrating ontology and evolving connectionist systems that is being developed in the Knowledge Engineering & Discovery Research Institute -Kedri.
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