##An ontology for biomimetics combining biology, engineering and evolution, providing design by analogy
Biology is gradually yielding lessons and ideas for technology, but the resulting innovation is adventitious. Biology is also very complex: currently with no underlying analytical model and so cannot adequately be interrogated by technologists. A concept which can bridge this gap is the trade-off, which leads to speciation in biology and aspects of design and problem-solving in engineering. An ontology is presented which uses biological organisms as case studies and TRIZ-like terms to define trade-offs and the factors by which they can be manipulated. The ontology, written in OWL2 in the Protege editor, is based on a collection of case studies taken from published research papers and reviews of biological trade-offs in which the factors that control or resolve the trade-off have been isolated and identified. I have used the Basic Formal Ontology (BFO) as the basis, originally in the hope that the ontology could be developed / corrected / refined to the standard that it can be made acceptable for inclusion in the OBOFoundry list. I have revised this hope in the light of experience and aimed for ease of use instead.
The terms used to define the trade-offs and their resolution are taken from the problem-solving system generated by Genrich Altshuller's project TRIZ, an acronym for (translated from the Russian) "theory of solving problems inventively". Specifically he defined problems as simple trade-offs (originally conceived as a sort of Hegalian dialectic) and classified them with a limited list of "Technical Parameters". These Parameters were derived to cover all problems which might be met in any context or system. Having defined a trade-off in this way, the resolution of each trade-off, derived from the case studies, is classified using the terms in another limited list of "Inventive Principles" which are essentially changes or additions that will enable the trade-off to be controlled and/or resolved. It is important that neither the trade-off nor its resolution are seen as related to any sort of 'optimum' but rather as a system described by a Pareto distribution which maps the best performance in a system that can vary as context changes. The aim is to define adaptability to changing circumstance. The terms in both lists are sufficiently general to have wide applicability, although each term is populated by more detailed and specific examples. Thus in making this ontology friendly to biology, both Parameters and Principles have definitions and changes which can be performed by biological entities and / or technical entities.