Model-driven, not data-driven

May 27, 2010

Here's a short definition of an ontology that I wrote up the website at work. There's a lot more that can be said, but I think the discussion of why ontologies are useful is of interest.

An ontology is a description of the entities in an area of interest, or domain, the attributes of those entities, and the relationships between them. This description is both formal, meaning it can be acted on by a computer, and human-readable.

One of the major strengths of an ontology is that it lets us organize information in terms of the problem we’re trying to solve, not the data we’re collecting. While data remains important in an ontology-based information system, it is structured according to the concepts of the domain, not the table structure of the database it’s stored in. This is important for two reasons: we can formalize relationships between pieces of data that would only be hinted at by foreign keys and naming conventions in a database. More importantly, it frees us to think about our problem space in terms of concepts and abstraction, not data. To take a model-driven approach instead of a data-driven one. Humans think in terms of models, not data. It is models that give meaning to data. As we deal with ever increasing volumes of data, it is models that help us identify what’s important, organize it, hypothesize about it, and discover connections between disparate data.


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