author: graeme c. simsion and graham c. witt chapter 12 physical database design

Author: Graeme C. Simsion and Graham C. Witt

Chapter 12Physical Database Design

Copyright: ©2005 by Elsevier Inc. All rights reserved. 2

Ontology and data modeling• In this lecture we consider how ontology

can help in data modeling– What is ontology?– How ontology can help data modeling?– What is it that ontology cannot do?– The data modeler: Creativity beyond

ontology construction

• First we examine how ontology is used in information systems


Categories of ‘Ontology’ in information systems

1. Highly general ontologies or ‘top-level’ ontologies used as a theoretical underpinning for modeling tools (such as ‘the ER model’) in information systems. Examples are Chisholm’s ontology, Bunge’s ontology, BFO (Basic formal ontology) and

2. Ontologies restricted to specific domains such as medicine, accounting, or geography (like specific data models).Ontologies for domains must facilitate automated data-sharing between specific fields and the automatic construction and population of ontologies developed in these fields.

But... What is ontology? Where does it come from? How can it help me in my company?


Highly General(philosophical) Ontology

• An ontology defines the most general categories (like ER) to which we need to refer in constructing a description of reality (akin to a data model such as ER), and it tells us how these categories are related.

• It describes reality without specifying the particulars of any category. It must further be able to be used to describe reality at any point in time (either now, or in the future, or in the past)

• It helps avoid errors in descriptions of ‘what there is’ in reality (part-of, abstraction, types, relationships)

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Domain-specific (philosophical) Ontology

• Philosophers also construct ontologies for domains such as medicine, geography or accountancy, with categories that are sufficient to support the representation of all that exists in the corresponding domain (akin to a specific data model about a domain)

• These domain ontologies are principally driven by philosophical theory but describe the complexities of ‘reality’.


Illustrating the Difference Using a Model

Hospital

Operation

OperationType

Surgeon

DrugAdmin

Drug

StandardDrug Dosage

be performed at

perform

operate be at operated

at by

manage

be classifyclassified by

follow

be followed by

use

be used in

be used in

use

be of be available in

be prescribed at

prescribe

bemanaged

by

• High-Level Ontology– Boxes, lines, ‘crows feet’

etc. are general ideas that can be applied to many different contexts

– Different modelling conventions can be compared (eg. UML vs. ER)

• Domain-specific ontology– The categories ‘Drug’,

‘Sandard Drug Dosage’ …, and the rules contained in the crows feet and other markings in the modeling convention.


Ontology and Data Modeling• In many ways, data modeling is doing ontology in a specific context

(similar to domain)• But, what can philosophy (ontology) tell us?

– The nature of the construction of social reality (plus physical reality if important)

– What the data that we have refers to (in reality)– How perspective and purpose affect the data model we have– But… it depends on the philosophy.

• What data are we interested in?– About things in which the company has an interest (people, other

companies, laws, etc.) but not necessarily one domain

• Which perspective(s) and what purpose(s)?– the company’s perspective and purpose, and– the purpose of the system for which the database is being designed

• For those interested… common-sense ontology is useful in discussing what exists (what constitutes reality) from a human-centered viewpoint.


Doesn’t this mean ‘one answer’?• No!

– Common-sense realism (as opposed to scientific realism) allows for perspective and purpose (Chisholm’s ontology is an example)

– Ontologies that help in this way will tell you when you have it wrong! But, not suggest ‘The one true answer’.

• Why? ‘reality’ for companies is not like physical reality: it is changeable and arbitrary (constructed) not governed by laws.

• We are not in the business of scientific analysis like chemistry or physics.

And critically… for each different company, the makeup of reality may be different as will perspective and purpose. (My company’s needs will be different from yours)


Let’s return to ‘What is data modeling?’

• Specification / design of (logical) data structures

• Database specification (from a user perspective)

• Identifying what data are to be held in a database and how it should be represented and organized

Architecture as Metaphor– Working with others– Analysis and design– Patterns– Compromise– Build on common

criticisms– Learning how to do it…

and how long it takes to be good at it


Can’t we expect the one ‘best’ answer when modeling?

• Surely, there is ‘one right answer’ when we model?– Not the case, even when considering the same simple

description

• Why not ‘one right answer’?– Different ‘trivial choices’ (naming etc.) or– Creative difference

• Creative difference can be because of– Different abstractions / classifications– Different levels of generalization– Rules held in different places

• Data structures• Code• Data• External to the database


Data modeling is a kind of classification (but not objective)

• You are designing a database through data modeling to classify data of interest to your company

• So, we have seen that ontology can…– deal with classification– handle the needs and perspective of the company and

its systems when classifying data– Help judge when your data model is non-sensical

• Be careful when using ontology: one size does not fit all!


What has been found about choice and creativity?

• Choice and creativity in modeling goes further to uncover true design in modeling

• Further research may show that this is the crux of creating good quality perspective includes esthetics, experience, and ‘good’ design.

• Graeme Simsion is researching choice and creativity in data modelling:http://www.simsion.com.au/research.htm

• There is choice and creativity in data modeling that goes beyond just naming or other trivial differences

• Where does ontology end and creativity begin?– When you fine-tune the perspective and generalize / abstract (perhaps using

patterns as a starting point) and when you place ‘business rules’ (Eg. In code vs. in data model).


Beware the Semantic Web• Because of all the reasons stated, the

semantic web is doomed to ultimately fail.• Why?

– The highly contextual nature of human activity and understanding

– The changeable nature of social reality and the culture-specific nature of social reality

• These will mean that, assuming the ontology is right (which is questionable), the semantic web rapidly falls into disrepair.


So, where to now?

• Enjoy applying some of the tips and tricks you’ve learned.

• Others are contained in ‘Data Modeling Essentials’ - apply them all

• Keep modeling and above all, rejoice in your creativity while applying (and learning) the essence of ‘good’ design in data modeling


Resources

General site on ontology and its practical application

http://ontology.buffalo.edu/

DOLCE and it’s domain ontologies

http://www.loa-cnr.it/

author: graeme c. simsion and graham c. witt chapter 12 physical database design

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