Hyponymy relation acquisition has been extensively studied. However, the informativeness of acquired hypernyms has not been sufficiently discussed. We found that the hypernyms in automatically acquired hyponymy relations are often too vague for their hyponyms. For instance, “work” is a vague hypernym for “work→Seven Samurai” and “work→1Q84”. These vague hypernyms sometimes cause the lower accuracy for NLP applications such as information retrieval or question answering. In this paper, we propose a method of making (vague) hypernyms more specific exploting Wikipedia. For instance, our method generates two intermediate nodes “work by Akira Kurosawa” and “work by film director” for a original hyponymy relation “work→Seven Samurai”. We show that our method acquires 2,719,441 hyponymy relations with the first intermediate concepts (such as “work by Akira Kurosawa”) with 85.3% weighted precision and 6,347,472 hyponymy relations with the second intermediate concepts (such as “work by film director”) with 78.6% weighted precision. Furthermore, we confirm that hyponymy relaitons acquired by our method can be interpreted as “object–attribute–value”.