Base placement is particularly critical for navigation manipulation tasks in environments, where improper placement can severely hinder task execution if the object’s kinematics are not properly taken into account. In this work, we present MoMa-Pos, a framework that determines the base placement for mobile manipulators in such environments. MoMa-Pos leverages a graph-based neural network to predict object importance and selectively reconstructs the environment by prioritizing task-relevant key objects, enhancing computational efficiency and ensuring that only essential kinematic details are processed. Moreover, MoMa-Pos integrates inverse reachability maps with environmental kinematic properties to determine feasible base placement tailored to the specific robot model. Extensive evaluations demonstrate that MoMa-Pos outperforms existing methods in both real and simulated environments, offering improved efficiency, precision, and adaptability across diverse settings and robot models.