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We propose higher order knowledge management platform (HKMP) that can support to provide the personalized services for the end users by means of evolving lower order network knowledge. HKMP helps 3rd party service providers to create personalized services considering user's context through the open interfaces for providing higher order knowledge. In this paper, we classify lower order network knowledge and build ontology to represent them including user profiles and user's preference. Furthermore, we propose the efficient algorithms of leaner and recommender to evolve higher order knowledge in HKMP. Finally, we simulated to evaluate the precision of proposed algorithms with data sets of UCI depository. As the result of evaluation, we expect that the HKMP would be an essential component for providing personalized services in next generation networks.