In this paper, we apply Inductive Logic Programming (ILP) to Japanese Translation Task of SENSEVAL2. Translation Task is regarded as a classification problem, and can be solved by inductive learning methods. However, we cannot use general statistical learning methods for this task, because this task has the serious problem that it is hard to create training instances newly. Therefore, the problem is how to learn a classifier from instances in Translation Memory, that is, small training data. To overcome this problem, we use ILP which can handle background knowledge in learning. This is a big advantage over statistical learning methods. Background knowledge means domain specific knowledge which are not described in training data clearly. Using background knowledge, we can learn rules through small training data. In this paper, we used Progol as a ILP system, and ‘bunrui-goi-hyou’ as background knowledge to achieve the precision 54.0% for Translation Task. This precision is superior to other systems in the contest which did not create new training instances.