Analysis of Information Assets

This is a white paper originally written in 2016 for a customer who was building asset tracking systems for information – i.e. tracking databases, documents, messages, backups, etc. There is something quite slippery about this kind of asset that makes it difficult to model. We decided to go back to first principles and work up from there. This (rather long) blog summarises what we found. This is not an easy topic, so not one for casual reading. Introduction Modelling information

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Object-Space and Name-Space

Some ontology background material first published in 2013 The purpose of a formal ontology is to provide a useful, and where possible, accurate model of the real world. Ontologies are made up of elements and relationships. Those elements and relationships are named. It is important to distinguish between the element and its name(s). In the example above we have a hierarchy of types and their names. The similarity between this and a taxonomy is clear, and from a simple example

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Enterprise Ontologies

This (long) blog was originally published as a chapter in the Springer Book – Intelligence-Based Systems Engineering When I first came here, this was all swamp. Everyone said I was daft to build a castle on a swamp, but I built it all the same, just to show them. It sank into the swamp. So I built a second one. That sank into the swamp. So I built a third. That burned down, fell over, then sank into the swamp.

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Introduction to the BORO Method

Originally written in 2010 for UK Government paper. BORO stands for Business Object Reference Ontology. The purpose of the method is to re-engineer disparate data sources into a common model. It is particularly good at semantic analysis – establishing whether two concepts are the same, if they overlap, or if they are unrelated. Traditional methods of data analysis tend to be linguistic, comparing the names of the concepts rather than the things they describe[1]. Some modern methods have introduced a

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