Data Modeling: Compare Techniques

In the previous post regarding data and the way that they could model, was made an attempt to visualise them through Gephi. In that post, it will be discussed the modeling of data and the different techniques that contribute to this, and which of these techniques could work better for a humanities dataset, according to my personal experience.


Data modeling is the process in which the structure of data is represented as well as the relationships that are created between data. This is the reason why all the notation systems that are used commonly have the ability to convert one to another. There are presented differences among them which are more aesthetics. However, with some of them, there is the opportunity to create and show differences that others cannot do it, and all of them do not have the same or all the symbols to represent all the possible situations. (Hay)

A data model can have many uses and implementations such as in the field of business and science. There are three different data model types, which are: conceptual data model, logical data model, and physical data model. Each of them can be independent of each other and render in schemas that named conceptual, logical and physical schemas respectively.

Firstly, a conceptual schema used for the representation of data in a database, describing the semantics of a domain or simpler the first part of the data requirements organising. Secondly, a logical schema represents the structure of a domain of information, capturing important information regarding the elements of the database and the way they related to each other. Thirdly, a physical schema describes the details regarding store the data.

16578658-Abstract-word-cloud-for-Ontology-with-related-tags-and-terms-Stock-PhotoIn addition, there is an another method of data representation that called ontology. Ontology is a model which introduces a relevant to domain vocabulary and specifies the intended meaning of vocabulary. Also, an ontology has two parts a set of axioms and a set of facts. The former set is used to describe the structure of the model, and the latter to describe some particular actual situation. (Horrocks)

When we talk about the structure of humanities data, the ontology could be a quite efficient method that could help in the creation of a humanities database. A schema which describes the humanities data could be large and complex or used at query times such as the data of a library or a museum, or an archive. This is connected directly with the type of data, which could be quite descriptive. In those cases, the metadata of an element could be many and different between them, and the way of each user could make a search differ and difficulty predictable as well. An ontology has the ability to create a reasonable structure, including inferred answers and intended queries. (Horrocks) That could help a lot the structure of humanities data as with an ontology some very basic problems of them could be solved.

Having tried to create an ontology in Protege, an ontology editor, I saw that it is not a very complicated process for a person with humanities and not science background to create an ontology. There is the opportunity to create and name your elements and their relationships too, giving the freedom to organise the data and the structure of them according to you and your users’ needs, and not following formworks created by others, that maybe do not feet in your aims. That is very important for people like me, to find a method that they can understand and work with it without problems.


Hay, D., C., A Comparison of Data Modeling Techniques,, Web, Accessed on 8/5/2017

Horrocks, Ian, Ontologies and Databases,…/Ian_Horrocks_Ontologies_and_databases.pdf, Web, Accessed on 8/5/2017

Seiner, R., S., Different Kinds of Data Models: History and a Suggestion,, Web, Accessed on 8/5/2017