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Catalyzing Inquiry at the Interface of Computing and Biology
and colleagues argue that “as models become more sophisticated, so does the representation of the data. As models become more capable, they extend our ability to explore the functional significance of the structure and organization of biological systems.”21
Variations in language and terminology have always posed a great challenge to large-scale, comprehensive integration of biological findings. In part, this is due to the fact that scientists operate, with a data- and experience-driven intuition that outstrips the ability of language to describe. As early as 1952, this problem was recognized:
Geneticists, like all good scientists, proceed in the first instance intuitively and … their intuition has vastly outstripped the possibilities of expression in the ordinary usages of natural languages. They know what they mean, but the current linguistic apparatus makes it very difficult for them to say what they mean. This apparatus conceals the complexity of the intuitions. It is part of the business of genetical methodology first to discover what geneticists mean and then to devise the simplest method of saying what they mean. If the result proves to be more complex than one would expect from the current expositions, that is because these devices are succeeding in making apparent a real complexity in the subject matter which the natural language conceals.22
In addition, different biologists use language with different levels of precision for different purposes. For instance, the notion of “identity” is different depending on context.23 Two geneticists may look at a map of human chromosome 21. A year later, they both want to look at the same map again. But to one of them, “same” means exactly the same map (same data, bit for bit); to the other, it means the current map of the same biological object, even if all of the data in that map have changed. To a protein chemist, two molecules of beta-hemoglobin are the same because they are composed of exactly the same sequence of amino acids. To a biologist, the same two molecules might be considered different because one was isolated from a chimpanzee and the other from a human.
To deal with such context-sensitive problems, bioinformaticians have turned to ontologies. An ontology is a description of concepts and relationships that exist among the concepts for a particular domain of knowledge.24 Ontologies in the life sciences serve two equally important functions. First, they provide controlled, hierarchically structured vocabularies for terminology that can be used to describe biological objects. Second, they specify object classes, relations, and functions in ways that capture the main concepts of and relationships in a research area.
220.127.116.11Ontologies for Common Terminology and Descriptions
To associate concepts with the individual names of objects in databases, an ontology tool might incorporate a terminology database that interprets queries and translates them into search terms consistent with each of the underlying sources. More recently, ontology-based designs have evolved from static dictionaries into dynamic systems that can be extended with new terms and concepts without modification to the underlying database.
M. Hucka, K. Shankar, D. Beeman, and J.M. Bower, “The Modeler’s Workspace,” 2002.
J.H. Woodger, Biology and Language, Cambridge University Press, Cambridge, UK, 1952.
The term “ontology” is a philosophical term referring to the subject of existence. The computer science community borrowed the term to refer to “specification of a conceptualization” for knowledge sharing in artificial intelligence. See, for example, T.R. Gruber, “A Translation Approach to Portable Ontology Specification,” Knowledge Acquisition 5(2):199-220, 1993. (Cited in Chung and Wooley, 2003.)