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For robots to interact with natural language and handle realworld situations, some ability to perform analogical and associational reasoning is desirable. Consider commands like” Fetch the ball” vs.” Fetch the wagon”, the robot needs to know that carrying a ball is (in the appropriate sense) analogous to dragging a wagon. Without the ability to perform analogical reasoning, robots are incapable of generalizing in the ways that true natural language understanding requires. Inspired by implicit Verlet integration methods for mass spring systems in physics simulations, we present a novel knowledge-based embedding method in this paper, where distributional word representations and semantic relations derived from knowledge bases are incorporated. We use some SAT-style analogy questions to demonstrate potential feasibility of our approach on the analogical reasoning framework.