Named entity linking (NEL) is an advanced technology which links a given named entity to an unambiguous entity in the knowledge base, and thus plays an important role in a wide range of Internet services, such as online recommender systems and Web search engines. However, with the explosive increasing of online information and applications, traditional solutions of NEL are facing more and more challenges towards linking accuracy due to the large number of online entities. Moreover, the entities are usually associated with different semantic topics (e.g., the entity “Apple” could be either a fruit or a brand) whereas the latent topic distributions of words and entities in same documents should be similar. To address this issue, this paper proposes a novel topic modeling approach to named entity linking. Different from existing works, the new approach provides a comprehensive framework for NEL and can uncover the semantic relationship between documents and named entities. Specifically, it first builds a knowledge base of unambiguous entities with the help of Wikipedia. Then, it proposes a novel bipartite topic model to capture the latent topic distribution between entities and documents. Therefore, given a new named entity, the new approach can link it to the unambiguous entity in the knowledge base by calculating their semantic similarity with respect to latent topics. Finally, the paper conducts extensive experiments on a real-world data set to evaluate our approach for named entity linking. Experimental results clearly show that the proposed approach outperforms other state-of-the-art baselines with a significant margin.