Admission control is a mechanism used to restrict access to a computer network to some flows based on the current utilization level of the network resource. By regulating the number of on-going flows, admission control aims at preventing over loading, congestion and performance collapses, so that, accepted flows receive a sufficient level of Quality of Service (QoS). In this paper, we evaluate three existing measurement-based admission control (MBAC) solutions, and we compare their efficiency in the context of semantic networks. Semantic networks refer to networks that autonomously acquire a knowledge on the on-going traffic as well as on any new incoming flow requesting admission. In this framework, we configure the three MBAC solutions in a way they have an identical target in terms of maximum tolerable packet loss rate or maximum tolerable packet queueing delay. We evaluate the solutions performance analytically or by simulation, and compare them to the "ideal" admission control. The results show that one solution, outperforms the others in meeting the target performance.