arranging and interrelating ideas and meanings in a network (rather than a tree), using formal semantics. These relationships can be applied to embedded data so that the instances which might appear in a database can be interrogated, arranged and rearranged in a variety of ways. This plasticity and openness to change is difficult to implement in a database, which requires the purpose and scope to be known and defined before it is populated with information. Thus, a database has always to be started from scratch and its structure is rarely re-usable. An ontology is inherently re-usable, and not only can the relationships between the ideas be refined easily, but the data can be stripped out and replaced. This makes it easy to share ontological structures, many of which are available on the Internet (the Gene Ontology is the prime example), leading to widespread standardization of expression and the integration of ideas and data at a community level. It also makes joining such a community attractive since quite a lot of the basic effort of providing a framework for the integration of data will have already been done. The most useful aspect of an ontology is that it is possible, using a theory-proving reasoner, to derive new information from the implicit relationships which were embedded in the ontology during its construction. This is because an ontology has more semantic power and freedom of expression. Freedom can be dangerous, so it is necessary to check the integrity of the ontology, but tools are available to do this. Ultimately, it is easy to generate and use an ontology with relatively little experience in computing. It would be very difficult to generate an ontology by writing the raw code, so an editor is used.
There are several editors, but probably the easiest to use is Protégé. It is Java-based and free to download from Stanford University (http:// protege.stanford.edu). There are two types of Protégé - Frames (Protégé 3.x) and OWL (Protégé 4.x). I prefer Protégé 4.x, which is largely the work of the University of Manchester in the UK. The embedded syntax is pretty strict, I always feel, but that, of course, means it is more difficult to make a mistake! OWL, surprisingly, stands for “Web Ontology Language” from the World Wide Web Consortium. An OWL ontology consists of Individuals, Properties and Classes. Individuals are grouped into Classes, and both can be related by Properties. It has a rich set of operators - e.g. intersection, union and negation - which makes it possible for concepts to be defined as well as described. Complex concepts can thus be built up from definitions of simpler concepts. Furthermore, the logical model allows the use of a reasoner that can check the mutual consistence of the statements and can recognize which concepts fit under which definitions. The reasoner can, therefore, help to maintain the proper hierarchy. This is particularly useful when dealing with cases where classes can have more than one parent. Why, then, do I want to use an ontology when nearly everyone else in biomimetics is using a database? The answer is crushingly simple: I tried using a database for what I was trying to do, and it didn’t work! Rather than, as most databases seem to do, produce a list of biological effects which might be, or can be, used in an engineering environment, I want to produce a system that can compare biology and engineer-
Zygote Quarterly: zq05 | Spring 2013 | ISSN 1927-8314 | Pg 107 of 170