Knowledge Graphs (KGs) have become the backbone of various
machine learning based applications over the past decade. However,
the KGs are often incomplete and inconsistent. Several representation
learning based approaches have been introduced to complete the missing
information in KGs. Besides, Neural Language Models (NLMs) have
gained huge momentum in NLP applications. However, exploiting the
contextual NLMs to tackle the Knowledge Graph Completion (KGC)
task is still an open research problem. In this paper, a GPT-2 based
KGC model is proposed and is evaluated on two benchmark datasets.
The initial results obtained from the _ne-tuning of the GPT-2 model
for triple classi_cation strengthens the importance of usage of NLMs for
KGC. Also, the impact of contextual language models for KGC has been
discussed.
