Lemmatization of the words is an important preprocess for Natural Language Processing (NLP) studies. Especially in language applications (such as part of speech tagging, spell-checking, and document clustering), selection of the right lemma with morphological features can provide better results. In this study, we present a new hybrid approach for Turkish inflected words by using morphological similarity based graph models which is recently getting popular in lemmatization. For this aim, a novel similarity function for Turkish is developed to connect the similar word forms. The proposed model is trained and tested by a double-checked Turkish lemmatization dataset. Then, empirical results are compared with ones of Zemberek which is the most used Turkish lemmatization tool.