This paper addresses the problem of mining a class of semantic networks, called Concept Frame Graphs (CFG's), for knowledge discovery from text. This new representation is motivated by the need to capture richer text content so that non-trivial mining tasks can be performed. We first define the CFG representation and then describe a rule-based algorithm for constructing a CFG from text documents. Treating the CFG as a networked knowledge base, we propose new methods for text mining. On a specific task of discovering the top companies in an area, we observe that our approach leads to simpler content mining algorithms, once the CFG has been constructed. Moreover, exploiting the network structure of CFG results in significant improvements in precision and recall.