  Pretrained Transformer models have emerged as state-of-the-art approaches
that learn contextual information from text to improve the performance of
several NLP tasks. These models, albeit powerful, still require specialized
knowledge in specific scenarios. In this paper, we argue that context derived
from a knowledge graph (in our case: Wikidata) provides enough signals to
inform pretrained transformer models and improve their performance for named
entity disambiguation (NED) on Wikidata KG. We further hypothesize that our
proposed KG context can be standardized for Wikipedia, and we evaluate the
impact of KG context on state-of-the-art NED model for the Wikipedia knowledge
base. Our empirical results validate that the proposed KG context can be
generalized (for Wikipedia), and providing KG context in transformer
architectures considerably outperforms the existing baselines, including the
vanilla transformer models.
