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The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and to support tools for functionality extensibility. In this paper we introduce an ontological approach to information integration and propose design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages (DSLs) generation and source code round-trip synchronization with DSL-models.