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The knowledge map describes who has what knowledge (tacit), where the knowledge collected, and helps to learn the jobs and expertise in organization. In many organizations there is a lack of directions to manage knowledge and knowledge map. Data classification is one technique using in knowledge mapping. This paper proposes an approach for knowledge mapping of experts in organization by comparing the performances of four classification algorithms. The classification proposed in this paper in the domain of energy expert. We measured prediction performances by comparing algorithms with four classification algorithms: two types of decision trees (ID3, C4.5) and two rule-based (OneR and Prism). These four algorithms are measured their effectiveness with K-fold cross-validation method on their classification correctness. The results show that C4.5 algorithm is the best one in decision tree, and Prism is the best one in rule-based. Among the four algorithms, C4.5 is the best performance in classification for knowledge mapping. Future research and implication are also suggested.