Ontology for Database Preservation

Ontology for Database Preservation

Elvira Locuratolo (ISTI CNR, Italy) and Jari Palomäki (Tampere University of Technology, Finland)
DOI: 10.4018/978-1-4666-1993-7.ch008
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Original research based on concept theory is exploited to define a concept structure called ontology for database preservation and to map this structure to database models. Starting from a general concept, corresponding to a Universe of Discourse, and from basic concepts intensionally related to the general concept, an engineering method allows the construction of ontology for database preservation. The leaves of this structure can be mapped into many equivalent graphs of classes supported by logical database models. Each of them can be encoded in the database in many different ways. This approach can be usefully exploited for database preservation. Moreover, all the concepts related to a class of the logical database model as well as all the consistent relationships among these concepts belong to the constructed concept structure.
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In concept theory (Kauppi, 1967; Palomäki, 1994), it is possible to make a distinction between the intensional/concept level, which is the level of human thinking, and the extensional/set-theoretical level, which is the level of computer science. At the intensional level, concepts are dealt with; whereas, at the extensional level, sets of objects/classes are considered (Locuratolo & Palomäki, 2008). A set of objects can be extension of many different concepts. As an example, a set of human beings can be the extension of non-identical concepts, such as “rational animals” “human,” and so on. As a set of objects falls under many different concepts, an arrow exists which is directed only from the intensional to the extensional level (Palomäki, 1994). Figure 1 puts into evidence the distinction between the intentional and the extensional aspects of a concept, where the former, represented by a cloud, is referred to the information content of the concept, whereas the latter, represented by an oval, is referred to the set of objects, which fall under the concept.

Figure 1.

From concept to objects

As the concept level and the object level are two really different levels, it is significant to make their distinction evident and to establish their relationships. In order to evidence the difference, let us observe that at the concept level concepts not existing in reality can be constructed. Correspondently, at the set theoretical level empty sets that will not be generated fall under those concepts. On the other hand, at the set-theoretical level, objects can be added to classes. This has no sense at the concept level.

Research developed in computer science (Elmasri & Navathe, 2000), such as in the field of conceptual modeling, with applications in databases, software engineering and formal methods (Krogstie, Halpin, & Siau, 2005), reduces the concept level and the object level to a single level, since the class name identifies also the concept name (Locuratolo & Palomäki, 2008). In methods of data analysis and knowledge representation, such as in the formal concept analysis method (Ganter & Wille, 1999) which has applications in linguistics, software engineering, psychology, artificial intelligence and information retrieval, the concept definition is based on the object definition; however, we know that concepts exist independently from objects, thus this perspective has to be extended. Although in concept theory formal background is provided to model concepts, the concept level is still an unexplored world. Until now, the link between concept theory and computer science has been at the extensional/set-theoretical level (Locuratolo & Palomäki, 2008). Recently, a methodology has been proposed to transport algorithms of objects decomposition at the concept level. This allows a link from the concept level to the mathematical/computer science level to be established (Locuratolo & Palomäki, 2011).

In this chapter, original research based on concept theory is exploited to construct a concept structure called ontology for database preservation, and to map this structure to the Universe of Discourse and to the database models. While substantial investment has been made in the past to preserve conventional forms of objects, such as documents, images and numerical data in some file format, the need to preserve entire databases has only recently emerged (Gladney, 2007). Databases differ from fixed digital objects studied in the past, since they change over time, have internal structure, and include schemas and integrity constraints, which are of interest for current and future interpretations.

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