Semantic query optimisation is a comparatively recent approach for the transformation of a given query into equivalent alternative queries using matching rules in order to select an optimum query based on the costs of executing of these alternative queries. The most important aspect of this optimisation approach is that this resultant query can be processed more efficiently than the original query. This paper describes how a near optimal alternative query may be found in far less time than existing approaches. The method uses the concept of a 'search ratio' associated with each matching rule. The search ratio of a matching rule is based on the cost of the antecedent and consequent conditions of the rule. This cost is related to the number of instances in the database determined by these conditions. This knowledge about the number of instances is available and can be recorded when the rules are first derived. We then compare search ratios of rules to select the most restrictive rules for the construction of a near optimum query. The technique works efficiently regardless of the number of matching rules, since resources are not used to construct all alternative queries. This means that transformation and selection costs are minimised in our system. It is hoped that this method will prove a viable alternative to the expensive optimisation process normally associated with semantic query optimisation.