Ontology-Based Semantic Models for Databases

Ontology-Based Semantic Models for Databases

László Kovács (University of Miskolc, Hungary), Péter Barabás (University of Miskolc, Hungary) and Tibor Répási (University of Miskolc, Hungary)
DOI: 10.4018/978-1-60566-242-8.ch048
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

A key characteristic of database systems is the layered structure and the accomplished independencies as is defined in the ANSI SPARC database reference model. This level-wise approach is also applied in the database development processes. According to the design triangle, the problem area to be mapped into the database is described by a human-oriented description at the initial phase of the design activity. Semantic data model (SDM) as a design tool uses concept-level elements in contrast to database schemas that are models at the logical and physical levels. The main role of semantic models is that they provide an abstract approach; they are easy to understand and they provide database independence.
Chapter Preview
Top

Introduction

A key characteristic of database systems is the layered structure and the accomplished independencies as is defined in the ANSI SPARC database reference model. This level-wise approach is also applied in the database development processes. According to the design triangle, the problem area to be mapped into the database is described by a human-oriented description at the initial phase of the design activity. Semantic data model (SDM) as a design tool uses concept-level elements in contrast to database schemas that are models at the logical and physical levels. The main role of semantic models is that they provide an abstract approach; they are easy to understand and they provide database independence.

There are different application areas for semantic models. An SDM can be used for example in

  • database schema design,

  • knowledge transfer,

  • database integration,

  • schema validation, and

  • knowledge representation.

In the history of database systems, several SDM models were proposed and used, such as the ER, IFO, ODL or UML models. Most of these models are strongly related to the underlying logical models and can not provide the required independency and generality. A new approach to create an efficient SDM for databases is the application of ontology-based description. According to our experiences, many experts on database management field are not aware of meaning of ontology. The aim of this article is to show and to explain the importance and role of ontology in design processes.

Top

Semantic Models

With a formal definition (CROSI, 2005), the database schema model Λ can be given as a tuple Λ(ΣΛ, ΡΛ), where ΣΛ is the structure (or signature) and PΛ is the integrity component. The ΣΛ component is built up from predefined structure elements. Taking the relational model as an example for Λ, ΣΛ contains the following elements: field (attribute), relation and database signatures. For a given ΣΛ, the set of corresponding schema instances is denoted by IΣΛ. An instance of a relational schema signature is usually given as a set of relation instances. The PΛ component contains elements to define integrity constraints on the IΣΛ set. An integrity rule is used to restrict the valid, permitted schema signature instances.

One of the earliest and most widely used SDM tools are the Entity-Relation (ER) and the Extended Entity-Relation (EER) models (Chen, 1976; Date, 1995). The main building blocks are entities, attributes and relationships. Entities can be considered as high-level structured concepts while attributes are the low-level atomic, or structured concepts. The EER model distinguishes three types of relationships: specialization among entities, encapsulation among entities and association between entities or attributes. In contrast to the structure oriented approach of EER, functional semantic models, like the IFO (Abiteboul, 1987) model, are based on global conceptualization. The IFO model provides a higher level of abstraction as it neglects logical schema elements like cardinality and some other integrity constraints. The ODMG ODL (Cattell, 1997) model is used to describe object databases in object-oriented environment. ODL provides a modeling language near to the logical level with many details related to OO concepts. One of the most widely used modeling language is the UML which provides a general purpose, object-oriented tool set to describe the different aspects of objects and classes. UML is a set of modeling components among which the class diagram can be mentioned as the main schema language. Considering the most frequently used data management oriented semantic and logical models, the following signature and integrity elements comprise these models. Table 1 is related to the semantic data models and Table 2 describes the comparison of logical data models.

Key Terms in this Chapter

Ontology: An explicit specification of conceptualization. A semantic data model that describes the concepts and their relationships. It contains a controlled vocabulary and a grammar for using the vocabulary terms. The ontology enables to make queries and assertions and reasoning. The most popular form to describe ontology is RDF and OWL.

RDF: A semantic data model that describes the world with elementary statements. A statement is a triplet having the following form: subject – predicate – object.

OWL: A language to describe web-ontologies. It uses an XML format and it contains a formal description logic component, too. It provides the following base functionalities: classification, type and cardinality constraints, thesauri, decidability.

Semantic Network: A graph for knowledge representation where concepts are represented as nodes in a graph and the binary semantic relations between the concepts are represented by named and directed edges between the nodes. All semantic networks have a declarative graphical representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge.

Semantic Data Model: A design tool for databases that uses concept-level language elements. The main role of semantic models is that they can provide an abstract approach; they are easy to understand and they provide database independence.

Semantic Operators: Operators that are based on the semantic content of the text. Specialization and synonyms are base examples of semantic operators.

Ontology Merging: The integration of heterogeneous ontology information sources. Due to complexity of integration, it usually includes several distinct processes based on heuristic approach. The main components of integration are: ontology mediation, ontology matching and ontology translation.

Complete Chapter List

Search this Book:
Reset