Hierarchical relationships play a key role in knowledge graphs. Particularly, large and well-known knowledge graphs such as DBpedia contain significant number of facts expressed with hierarchical relationships in comparison to the other types of relationships. These hierarchical relationships are extensively harnessed by applications such as personalization, question answering, and recommendation systems. However, the presence of large number of facts with hierarchical relationships makes the applications computationally intensive. Additionally, the applications can be domain-specific and may not require all the hierarchical facts available, but only require those that are specific to the domain. In this paper, we present an approach to extract domain-specific hierarchical subgraph from large knowledge graphs by identifying the domain-specificity of the categories in the hierarchy. Given a domain, the domain-specificity of categories are determined by combining different types of evidence using a probabilistic framework. We show the effectiveness of our approach with a recommendation use case for movie and book domains. Our evaluation demonstrates that the domain-specific hierarchical subgraphs extracted by our approach can reduce the baseline subgraph by 40% to 50% without compromising the accuracy of the recommendations. Furthermore, the presented approach outperforms the recommendation results obtained with a state-of-the-art domain-specific subgraph extraction technique which uses supervised learning.