Applications and Types of Bio-Ontologies

A common ideal for an ontology is that it should be re-usable [6]. This ambition distinguishes an ontology from a database schema, even though both are conceptualisations. For example - a database schema is intended to satisfy only one application, but an ontology could be re-used in many applications. However, an ontology is only re-usable when it is to be used for the same purpose for which it was developed. Not all ontologies have the same intended purpose and may have parts that are re-usable and other parts that are not. They will also vary in their coverage and level of detail.

We can divide ontology use into three types:

1. Domain-oriented, which are either domain specific (e.g. E. coli) or domain generalisations (e.g. gene function or ribosomes);
2. Task-oriented, which are either task specific (e.g. annotation analysis) or task generalisations (e.g. problem solving);
3. Generic, which capture common high level concepts, such as Physical, Abstract, Structure and Substance. This can be especially useful when trying to re-use an ontology, as it allows concepts to be correctly or more reliably placed. It can also be important when generating or analysing natural language expressions using an ontology. Generic ontologies are also known as `upper ontologies', `core ontologies' or `reference ontologies'.

Most bio-ontologies will have a mixture of all three of these types in their ontology. A well-formed ontology will be built in a modular way using a mixture of generic domain, generic task and application ontologies. Its parts will be clearly defined so that they can be re-used. An less well-formed ontology will have these distinctions blurred, making re-use and modification more difficult. The measure of how well the dependencies in an ontology have been separated is known as its ontological commitment. Other measures for the quality of an ontology include its clarity, consistency, completeness and conciseness [6].

Ontologies are used in a wide range of application scenarios [10]:

1. A community reference - neutral authoring. The knowledge is authored in a single language, and converted into a different form for use in multiple target systems. Benefits include knowledge re-use, improved maintainability and long term knowledge retension;

2. Either defining database schema or defining a common vocabulary for database annotation - ontology as specification. Describing a protein entry as `mitochondrial double stranded DNA binding protein' will ensure that a common vocabulary is available for description, sharing and posing questions (see item 4 in this list). Benefits include documentation, maintenance, reliability, sharing and knowledge re-use;

3. Providing common access to information. Information must be shared but is expressed using unfamiliar vocabulary. The ontology helps to render the information intelligible by providing a shared understanding of the terms or mapping between the terms. Benefits include interoperability, and more effective use and re-use of knowledge resources;

4. Ontology-based search by forming queries over databases. An ontology is used for searching an information repository. For example, when searching databases for `mitochondrial double stranded DNA binding proteins', all and only those proteins will be found, as the exact terms for searching can be used. Whether the user of the terms can be sure of their meaning depends on how the knowledge in the ontology has been represented. For example, is it explicit that the `mitochondrial' applies to the `DNA' or the `binding protein'?

   Queries can be refined by following relationships within the ontology, for example, following relationships to find those processes in which proteins of certain functions act and gathering the associated proteins. Moving up and down the `is a kind of' hierarchy within the ontology can also be used to refine queries. For example, specialising `DNA binding protein' to `single stranded DNA binding protein' by moving down the hierarchy when the former gathered too many answers. Benefits include more effective access and hence more effective use and re-use of knowledge resources;

5. Understanding database annotation and technical literature. These ontologies are designed to support natural language processing (NLP) that not only link domain knowledge but also how knowledge is related to linguistic structures such as grammar and lexicons.

Although some methodologies are beginning to emerge that compare the structure and role of various ontologies [11], none have appeared that compare the content of one ontology with another for a specific domain.