  Link prediction task is vital to automatically understanding the structure of
large knowledge bases. In this paper, we present our system to solve this task
at the Data Science and Advanced Analytics 2023 Competition "Efficient and
Effective Link Prediction" (DSAA-2023 Competition) with a corpus containing
948,233 training and 238,265 for public testing. This paper introduces an
approach to link prediction in Wikipedia articles by formulating it as a
natural language inference (NLI) task. Drawing inspiration from recent
advancements in natural language processing and understanding, we cast link
prediction as an NLI task, wherein the presence of a link between two articles
is treated as a premise, and the task is to determine whether this premise
holds based on the information presented in the articles. We implemented our
system based on the Sentence Pair Classification for Link Prediction for the
Wikipedia Articles task. Our system achieved 0.99996 Macro F1-score and 1.00000
Macro F1-score for the public and private test sets, respectively. Our team
UIT-NLP ranked 3rd in performance on the private test set, equal to the scores
of the first and second places. Our code is publicly for research purposes.
