Ontology learning from relational database and reuse it with popular vocabularies

Abstract

Ontologies are the web documents generated by Web Ontology Languages that are used to develop semantic web. Semantic web requires data either in terms of manual creation or through conversion from existing data. A manual method for building ontologies is a very complex process and time-consuming. It also requires domain expert‘s need to understand the syntax and semantics of ontology development languages. It can also generate an error prone ontology. The data in the form of structured (relational) are used for ontology learning because they are most valuable data source available on the web. The research work for creating automatic ontology from structured database is not new. For this research work, many tools and methods were created to solve this type of problem. The primary limitation of the existing tools and methods for learning ontology from relational database is that the generated ontology is a simply copy of input database schema. This type of generated ontology gives the information from database schema and it does not contain any information about data. In this thesis we propose a tool for the automatic creation of ontology that gives the information about relational schema model and also the data stored in a database. Our aim is to analyze existing tools that were used for creating automatic ontology from relational databases and identify the advantages and disadvantages of these tools so that effective and valuable tool can be proposed. We have given detailed analysis of different existing tools used for creating automatic ontology from relational databases based on database schema and data stored in database and also performed a comparative analysis of these tools with our proposed tool.