Ontologies are more commonly used today but still little consideration is given of how to efficiently store them. The proposed approach is built on reliable and efficient relational database management systems (RDBMS). It can be easily implemented for other systems and due to its vendor independence existing data can be migrated from one RDBMS to another relatively easy.
TopIntroduction
During the last couple of years ontologies moved into the center of interest in mainstream computer science research. With the Internet becoming a truly global information resource, the effort required to find the right information increased, even though the quality of search engines improved considerably.
The next big step is anticipated to be the integration of semantic information of electronically available resources which will allow searches to obtain much better results. The process of building the required ontologies can either be top-down or bottom-up.
The RDF-based approach, favored by Tim Berners-Lee, strives to semantically enrich each Web page and build ontologies by integrating all the semantic information. Topic Maps, in contrast, are usually regarded as top-down approach where occurrences are linked to topics once the Topic Map exists.
A prerequisite to building large ontologies is an efficient way of storing the required data. Today, it is generally agreed that ontologies evolve over time and require maintenance. Thus both retrieval and updates need to be handled efficiently by the storage system.
In this chapter we present an improved database schema to store ontologies. More specifically, our contribution is to:
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Propose an intuitive and efficient way of storing arbitrary relationships (Section 2.1)
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Show that our database schema is well suited to store both RDF and Topic Maps (Section 2.2)
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Explain why it is more efficient by comparing it to other approaches (Section 3)
Link-Based Schema
In this section we first explain the general idea of the improved database schema and provide an example of how concepts and relationships between them are stored. We then show how both RDF and Topic Maps can be stored, too.