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Knowledge graph is becoming an indispensable resource that offers structured information for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. However, such methods fail to generalize on entity pairs that are not highly co-occurred, and fail to discover entity pairs that are not co-occurred at all in the given text corpus. We introduce a generative perspective to approach this task and define each relationship by learning the data distribution that embodies the core common properties for relational reasoning. A generative model KGGen is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the text corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that the proposed model KGGen generates novel and meaningful triplets with improved efficiency and less human annotation comparing with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]