Modeling techniques for ontological concepts


In large corporations such as aerospace and automobile industries or even banks there is a lot of technological data that has to be stored in large databases. Forming a concept out of such complicated data and relating them in some way is the basic idea behind Ontology. It basically is the science of categorizing data (W3 Website). Tim Berners-Lee from W3 Group states that creating websites is great enough but producing semantic web pages that contain more than static web content. Meaningful and semantic web content means to create links and data that can be easily searched and explored by other people and websites. (Tim Berners-Lee, July 2007)

The Greek philosopher Aristotle and his pupils were the first to use this science of Ontology (Formal Ontology Website). Categories do not exist on their own but are created to make more easy classifications. The human beings categorize the data available, so that we can relate instances in one category with an instance in another which enables us to remember things better.

The importance of meaningful data is very critical for integration of heterogeneous application in organizations. The lack of creating semantic content by the employees of the organization is attributed to lack of understanding the data structure, ease of use, and technology understanding (Conroy, 2007). He adds that people think Ontology as a very formal logical process and a complex procedure but it is the creation of metadata for data and information storage in an organization.

Growth of Ontology for Database Design

The growth of distributed databases in an organisation is growing with the increased focus on growth, specialized softwares, need for competitive advantage and globalization. Ontology databases have grown to categorize data and to provide a mechanism to link complex databases between various systems in an organisaton. Ontology should be focused to provide a brief, accurate and clear description of the database models and the database concepts should be related to the domain interest. Ontologies are designed to be reused and shared by different systems and processes.

Ben Necib and Freytag (2004) state that meaningful data for data manipulation has to be generated by meta models, integrity constraints and semantic rules which can be applied to overcome issues that are created by growth of data in local databases, growth of distributed databases across the organisation and the change in format in the internal systems in an organisation. They add that the capabilities of the query processing in a information centric organisation is done through modifying database queries into other semantic forms that are easily run in lesser time and with lesser resources.

Gruber(1993) defines ontology as "an explicit specification of a conceptualization" and many other researchers like Gaurino (1995) add that conceptualization is a very vague term in the definition by Gruber. Gaurino (1995) explains that ontology can be developed with many real case scenarios and combine the existing data concepts and instances in an organisation.

In case of a bio-technology or medical firm that are many supporting systems consisting of various data sources, diversity of data in terms of chemicals used, genes, diseases, research, doctor information are all diversely kept in separate databases (Critchlow, 1998). Silvescu (2001) add that these bio-technology firms should use advanced data pooling with a through understanding of their database designs and find similarity in their diverse databases that can all be assimilated and integrated.

Relational Database Model (RDBMS) and Object Oriented Database Management Systems (OODBMS)

The Relational Database Management System (RDBMS) was invented by IBM in 1970. (Database System Website) The precedents for this were hierarchical and the network models. Ted Codd laid down the rules for relational databases and normalization. (Channel Web Website, "Ted Codd, Chris Date")

RDBMS has a simple design and basically states that all data must be stored in tables. Users cancreate and access data easily. Once a database is created, more categories can be added without changing the existing database. The RDBMS systems have certain limitations like not having enough storage area to store audio and video. And it cannot operate with languages other than SQL. (AspFree Website)

The Object Oriented Database Management Systems (OODBMS) were designed for handling big and complex data that relational databases were finding difficult to maintain. OODBMS combined object oriented concepts of software engineering into the database technology (ODBMS Group Website). The four main concepts of object oriented programming namely inheritance, data encapsulation, object identity, and polymorphism. Previously defined objects are reused and inherited; this is the main objective of inheritance.

With the rising challenge of the internet and millons of users on the web today it became more of a necessity to build object-oriented databases. It is better known as OODBMS in short, and was created in order tohandle big and complex data which an RDBMS could not. This was an advancement over relational databases and bigger and more complex relations and tables are inherited, data encapsulated and hence easily created.

Inheritance allows the programmer to develop answers to complex problems by creating new objects by extending previously defined objects thus making reusability possible. Data encapsulation on the other hand hides the internal state of the objects. They can be accessed only by their methods and not internal states. There are totally three types of encapsulated objects namely full encapsulation, write encapsulation and partial encapsulation. (Search Oracle Web site)

Object identity all objects in of the database to be independent. Polymorphism and dynamic binding means you can define operations for one object share the specification of the operation with all other objects in the database.

Advantages and Disadvantages of RDBMS and OODBMS

The development of seemingly integrated application for a large corporation that has heterogeneous environments in different location and having linked and separate applications, the consolidation and reporting of data can be very difficult. The cost and time required to integrate large and complex relational databases can be high and the time required to map different tables and relations in a RDBMS requires good understanding of the complex database schemas and the database design.

Main disadvantage of RDBMS is that there is a reduced understanding and representation of real business objects and there is an incomplete use of business constraints and integrity principles. RDBMS supports same kind of data structure and there are reduced operations possible. (ODBMS Website - Disadvantages of OODBMS)


  • Ben Necib, Chokri and Freytag, Johann-Christoph, (2004), "Eighth East-European Conferenceon Advancesin Databases and Information Systems", 22-25 September, 2004,Budapest, Hungary
  • Gruber, T.: A translation approach to portable ontology specifcations. In: Knowledge Acquisition (5) No. 2, USA. (1993) 199-220
  • Guarino, N., Giaretta, P.: Ontologies and knowledge bases: towards a terminological clarification. In: Knowledge Building Knowledge Sharing, ION Press. (1995), 25-32
  • T. Critchlow, M. Ganesh, R. Musick. Automatic Generation of Warehouse-Mediators Using an Ontology Engine. In Proceedings of the 5 th Interna-tionalWorkshop on Knowledge Representation meets Databases (KRDB'98). May 1998.
  • Silvescu, A., Reinoso-Castillo, J., Andorf, C., Honavar, V., and Dobbs, D. (2001). Ontology-Driven Information Extraction and Knowledge Acquisition from Heterogeneous, Distributed Biological Data Sources. In: Proceedings of the IJCAI-2001 Workshop on Knowledge Discovery from Heterogeneous, Distributed, Autonomous, Dynamic Data and Knowledge Sources.
  • W3 Website,
  • Tim Berners-Lee from W3 Group," Linked Data", Website:, July 2007
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  • Database System Website :
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