Topic models are widely used for extracting latent features from documents. Conventional count-based models like LDA focus on co-occurrence of words, neglecting features like semantics and lexical relations in the corpora. To overcome this drawback, many knowledge-enhanced models are proposed, attempting to achieve better topic coherence with external knowledge. In this paper, we present novel probabilistic topic models utilizing both explicit and implicit knowledge forms. Knowledge of real-world entities in a knowledge base/graph are referred to as explicit knowledge. We incorporate this knowledge form into our models by entity linking, a technique for bridging the gap between corpora and knowledge bases. This helps solving the problem of token/phrase-level synonymy and polysemy. Apart from explicit knowledge, we utilize latent feature word representations (implicit knowledge) to further capture lexical relations in pretraining corpora. Qualitative and Quantitative evaluations are conducted on 2 datasets with 5 baselines (3 probabilistic models and 2 neural models). Our models exhibit high potential in generating coherent topics. Remarkably, when adopting both explicit and implicit knowledge, our proposed model even outperforms 2 state-of-the-art neural topic models, suggesting that knowledge-enhancement can highly improve the performance of conventional topic models.