A Micro-Genetic Algorithm for Ontology Class-Hierarchy Construction

Several methods for ontology development have been proposed. However, the development of domain ontologies is still carried out in an ad-hoc manner. This paper explores the use of a microgenetic algorithm with a seeding scheme based on hierarchical clustering for ontology class hierarchy construction. The microgenetic algorithm (μGA) is composed of an inner loop and an outer loop. The inner loop consists of: the evaluation of the fitness of each member of the population; the selection of parent chromosomes; the generation of a new population by using crossover and mutation operations; and the separation of the best-fit individual after convergence. The outer loop consists of creating a new random population, transferring the best individual from the inner loop, and restarting the inner loop. The fitness function is based on the correlation between the pair-wise similarities based on the semantic similarity measure of Wu-Palmer and those obtained using Internet and the normalized Google distance (NGD). The proposed approach was tested on the construction of a class hierarchy of machining processes. The results indicate that accurate class hierarchies can be obtained and convergence can be achieved fast with little memory to store the population.