Solving problems of data heterogeneity, semantic heterogeneity and data inequality : an approach using ontologies

Abstract

Knowledge is people’s personal map and people’s personal model of the world. Knowledge acquisition involves complex cognitive processes such as perception, communication, and reasoning. According to the knowledge differences, then it is possible for people have a different perception to attain awareness or understand the environment or reality. This paper provides a case study where there is a group of people in different communities managing data using different perceptions, different concepts, different terms (terminologies), and different semantics to represent the same reality. Perceptions are converted into data, and then saved into separate storage devices that are not connected to each other. Each user – belonging to different communities - use different terminologies in collecting data and as a consequence they also get different results of that exercise. It is not a problem if the different results are used for each community, the problem occur if people need to take data from another communities, sharing, collaborating and using it to get a bigger solution. In this paper we present an approach to generate a common set of terms based on the terms of several and different storage devices, used by different communities, in order to make data retrieval independent of the different perceptions and terminologies used by those communities. We use ontologies to represent the knowledge and discuss the use of mapping and integration techniques to find correspondences between the concepts used in those ontologies. We discuss too how to derive a common ontology to be used by all the communities. We can find in literature several documents about the theoretical concepts and techniques that can be used to solve the described problem. However, in this paper we are presenting a real implementation of a system using those concepts.