Intelligent agents help to automate time and resource consuming tasks such as anomaly detection, pattern recognition, monitoring and decision-making. One of the major issues in automation of cyberspace is the discordance between the concept people use and the elucidation of the corresponding data by existing algorithms. Moreover, the measurement and computation of relevance referred to as degree of match-making is a crucial task and presents one of the most important challenges in unknown and uncertain environments of multi-agent systems. Optimal algorithms that generate the best matches for a user input are desired. This paper overcomes the challenges listed by proposing an agent-based semantic match-making algorithm that addresses the problem of heterogeneous ontology at user end and semantically enhances the user-input. A degree of match-making evaluation scheme based on fuzzy logic is proposed and evaluated using synthetic data from the web. The results are found to be consistent on the scale provided by the existing algorithms.