A typical online knowledge-sharing environment would generate vast amount of formal knowledge elements or interactions that generally available as textual documents. Thus, an effective management of the ever-increasing volume of online knowledge documents is essential to organizational knowledge sharing. Reply-semantic relationships between knowledge documents may exist either explicitly or implicitly. Such reply-semantic relationships between knowledge documents, once discovered or identified, would facilitate subsequent knowledge access by providing a novel and more semantic retrieval mechanism. In this study, we propose a preliminary taxonomy of reply-semantic relationships for documents organized in reply-replied structures and develop a SEmantic Enrichment between Knowledge documents (SEEK) technique for automatically annotating reply-semantic relationships between reply-pair documents. Based on the content-based text categorization techniques and genre classification techniques, we propose and evaluate different feature-set models, combinations of keyword features, POS statistics features, and/or given/new information (GI/NI) features. Our empirical evaluation results show that the proposed SEEK technique can achieve a satisfactory classification accuracy. Furthermore, use of keyword and GI/NI features by the proposed SEEK technique resulted in the best classification accuracy for the Answer/Comment classification task. On the other hand, the use of keyword features only can best differentiate Explanation and Instruction relationships.