Ontologies: formalising biological knowledge for bioinformatics

Citation: Jonathan Bard. Ontologies: formalising biological knowledge for bioinformatics. Bioessays, May 2003, 25(5):501-506.
Link: NCBI PubMed

Summary

Ontologies are becoming increasingly important in bioinformatics because they can be linked to the information in databases and their knowledge then used to query the databases. This direct connection allows for faster searching in databases and less ambiguity than in string-based searches. Also, lots of data contains hierarchical relationships and relational databases do not handle hierarchies very well. The result is rich ontologies, which are independent of their associated databases and linked to them through term IDs.

The Gene Ontology (GO) is used to integrate genetic data about gene products with our knowledge of their properties. The GO catalogues its knowledge in three essentially non-overlapping ways: their location within cells, the process to which they contribute, and the functions they fulfill.

Semantic Memory

Citation: Quillian, M. Semantic Memory, in M. Minsky (ed.), Semantic Information Processing, pp 227-270, MIT Press, 1968.
Link: Book @ MIT Press Book @ ACM Portal

Summary

The central question is: what constitutes a reasonable view of how semantic information is organized within a person's memory? In other words, how can meanings or words be stored so that human-like use of these meanings is possible? This text proposes a model for such a memory structure, and explains its use in memory dependent tasks: 1) to compare and contrast meanings of two familiar English language words, 2) processing of English text to 'understand' it.

A library of generic concepts for composing knowledge bases

Citation: K. Barker, B. Porter, and P. Clark. A Library of Generic Concepts for Composing Knowledge Bases. First International Conference on Knowledge Capture, October 21-23, 2001.
Link: ACM Portal

Summary

Building a knowledge base traditionally involves a domain expert and a knowledge engineer. A goal of this research is to eliminate the knowledge engineer from this process, that is, to enable domain experts to build their own knowledge bases. A claim of authors' research is that users without knowledge engineering background will be able to represent knowledge from their domain of expertise by using generic components from a small library.

The authors have chosen to build a small library of components, and aim to use composition of these components as the means to achieve coverage, rather than through enumeration of a large number of components.

The research questions are: is such a system (1) easy to master for users not trained in knowledge engineering?, (2) and sufficient to represent sophisticated domain knowledge?

There are three requirements for library components:

• Coverage: The library should contain sufficient components in the library to allow the user to encode a variety of knowledge from any domain. This means defining a restricted set of components which are generic enough to allow a user to compose them consistently, but also be specific enough to allow use in individual domains.
  
• Access: The interface to the library should assist the user in finding appropriate components from the library.
  
• Semantics: The components in the library should have well defined axioms that encode their meanings as well as information about how the component can consistently interact with other components.
  

The library consists of entities and events (states and actions, where states are relatively static situations brought about or changed by actions).

Composition in this system is the ability to connect components in a way that allows inferences beyond the union of individual axioms of the components involved. By using composition, inferences like conditional rules, definitions (reclassification of instances) and simulations can be drawn. However, to achieve these inferences, composition language (relations and properties) must have predictable semantics. Relations connect entities and events (event-entity, entity-entity, event-event, entity-role). Properties link entities to values (cardinal, scalar, categorical etc.)

Evaluation of this library was done through user sessions and user feedback. The criteria for the feedback were (1) ease of finding relevant components, (2) understanding components, (3) use of components to represent knowledge, (4) ease of relations language, and (5) cast biological knowledge in terms of components and relations in the library. The library showed promising results.

Ontologies in Biology: Design, Applications and Future Challenges

Citation: Jonathan B L Bard, Seung Y Rhee. Ontologies in Biology: Design, Applications and Future Challenges. In Nature Reviews: Genetics, 2004.
Link: Nature Reviews
Status: Incomplete

Summary

Until recently, the most important task of bioinformatics was thought to be the storage, retrieval and analysis of molecular data. However, as experimental technologies move from producing relatively simple data to more complex data, we need comparable advances in bioinformatics to manage and relate these data. There is also a great deal of sophisticated biological knowledge, often hierarchical in nature, that needs to be integrated with other data. One way to represent such biological knowledge is by using ontologies. The resulting biological ontologies are formal representations of areas of knowledge in which the essential terms are combined with structuring rules that describe the relationship between the terms. Knowledge that is structured within a biological ontology can then be linked to the molecular databases.

For any ontology to be of public value, it has to be widely disseminated and accepted by the field that it aims to summarize. Sociological factors are important in ontology production and acceptance, and a strong community involvement is also crucial.

Definitions

Phenotype: The observable traits or characteristics of an organism, for example hair color, weight, or the presence or absence of a disease. Phenotypic traits are not necessarily genetic.
Systematics: This is an umbrella term to describe the processes that describe species. There are three disciplines which are united under this broad locution: description of species (identification), the naming of names (taxonomy) and description of the relationships among and between taxa (phylogenetics).