Now, we briefly describe the remaining chapters of this thesis as follows:
In Chapter 2, first, we introduce the Semantic Web, especially for the ontology. Sec-ond, due to the problem of the deep Web the deep annotation framework is described. The goal of this framework is to propose a solution to map or migrate the databases behind the deep Web to the ontologies. Third, we introduce two main approaches of Mapping between Relational Databases and OWL ontologies including Extracting OWL ontologies from Relational Databases and Mapping Relational Databases to Existing OWL Ontolo-gies. The latter case is our objective in this thesis. Finally, we describe some matching techniques that assist greatly the mapping between databases and ontologies.
In Chapter 3, since our object is to map a relational database to existing OWL ontologies we first examine the basic concept of Web Ontology Language (OWL) and relational database. Then the difference between databases and ontologies are described.
Finally since OWL is based on the Description Logics (DLs) we also illustrate the mapping between OWL DL and DLs.
In Chapter 4, we introduce our approach to directly mapping a relational database to OWL ontologies. We first describe our terms and functions of the relational database and OWL ontologies used in our approach. Then the detailing of approach is discussed.
In Chapters 5 and 6, a mapping system “Annotator” implementing the approach
proposed in this thesis, our contributions and future works are introduced.
Chapter 2
Related Work
In this chapter, we will introduce some related studies in mapping between a relational database and OWL ontologies. Our object is to map a relational database to domain-related OWL ontologies to realize the requirement for the Semantic Web. Thus, we begin to discuss the Semantic Web and his enabling technology ontology. Then the researches of the deep annotation and the database-to-ontology mapping are described. Finally, we introduce some matching techniques that do a lot of assistance in these fields.
2.1 Semantic Web
Since the development of the Web technologys, Web pages has increased rapidly. Ac-cording to Google, currently the number of Web pages has well over 15.5 billion [32] . These Web pages contained all kinds of information, however, the vast majority of these information is only in a human understandable format such as HTML. More precisely for-mat is eXtensible Markup Language(XML). Although XML provides a set of self-defined metadata tags to describe the semantic of Web data, it does not define the meaning of the tags. As a consequence, Web content can be accessed only in the syntactic level and software agents or machines can not efficiently understand and process this kind of data.
For this purpose, in 2001 [7] , Tim-Berners Lee proposed the vision of the Semantic Web as follows: “The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” To realize the requirement for the Semantic Web, World Wide Web Consortium(W3C) and other organizes have been effected at specifying and developing standard language. The Semantic Web will built on the standard layers as
Figure 2.1: Semantic Web Layers
shown in Figure2.1 [22]. In next section, we will present ontology which is the core component in these layers.
2.1.1 Ontology
The term “Ontology” came from philosophy and there are plenty of definitions, the most popular is “An ontology is a formal, explicit specification of a shared conceptualization”
[17] . Ontology is a key enabling technology for the Semantic Web. Unlike the XML, the level ontologies provides not only the syntactic but the semantic. By ontologies, we can build a controlled vocabulary of concepts, each with explicitly defined and machine-understandable semantic. Thereby, people and machines can communicate precisely.
For this reason, during the last decade interest to ontologies has increased. The area of applicability for ontologies is wide: information retrieval and extraction, information systems design and enterprise integration, natural language processing, database design, conceptual modeling. In the Semantic Web area, a formal knowledge representation model is also called an ontology.
However, constructing ontologies for domain knowledge is still tedious, time-consuming and error-prone. Although some tools like Protege [38] can manually edit ontologies, fully automatic ontology building remains in the distant future. In order to make the ontology-construction efficient, Maedche and Staab presented an ontology-learning framework [27]
that encompasses ontology import, extraction, pruning, refinement, and evaluation. The
overview of framework is as follows :
• Merging existing structures or defining mapping rules between these structures allows importing and reusing existing ontologies.
• Ontology extraction models major parts of the target ontology, with learning sup-port fed from Web documents.
• The target ontology’s rough outline, which results from import, reuse, and extrac-tion, is pruned to better fit the ontology to its primary purpose.
• Ontology refinement profits from the pruned ontology but completes the ontology at a fine granularity (in contrast to extraction).
• The target application serves as a measure for validating the resulting ontology.
Furthermore, in order to have the consistent format to model ontology, W3C had defined the standard for Web ontology that is OWL(Web Ontology Language) [29]. OWL is a language for ontology serves as defining and conceptualizing the Web content. We can conceptualize or model a domain knowledge through OWL features such as class and property. Currently, OWL is built on top of RDF(Resource Description Framework) and divided into three sub-languages depend on the expressiveness, namely OWL Lite, OWL DL and OWL Full. In Chapter 3, we have the details for OWL.