Hierarchical graphs are a frequent solution for capturing symbolic data due the importance of hierarchies for defining knowledge. In these graphs, relations among elements may contain large portions of the element's semantics. However, knowledge discovery based on analyzing the patterns of hierarchical relations is rarely used. We outline four inference based algorithms exploiting semantic properties of hierarchically represented knowledge for producing new links, and test one of them on a generalization of Cyc's KB. Finally, we argue why such algorithms can be useful for unsupervised learning and supervised analysis of a KB.