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Knowledge representation and pattern retrieval are the basis of knowledge discovery and reasoning. Different from many knowledge representation models such as production rules, graph model used to present context information in text has been envisioned as an appropriate solution to solve complex relevance more acceptably by the user. In this paper, a novel graph model, feature event dependency graph (FEDG) is proposed. FEDG emphasizes on representing the fact level knowledge compressively without losing important information. Meanwhile, based on this model, we propose retrieval and rank strategies for knowledge pattern retrieval which is meaningful for effective reasoning and latent knowledge discovery on large volumes of text knowledge. Extensive experiments on real knowledge sets, containing hundreds of domain specific rule based knowledge, demonstrate the feasibility and effectiveness of the proposed scheme.