  Link prediction is an important task in addressing the incompleteness problem
of knowledge graphs (KG). Previous link prediction models suffer from issues
related to either performance or explanatory capability. Furthermore, models
that are capable of generating explanations, often struggle with erroneous
paths or reasoning leading to the correct answer. To address these challenges,
we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and
Slow Thinking with Filtered rules for Link Prediction task), inspired by two
distinct aspects of human cognition: "commonsense reasoning" and "thinking,
fast and slow." Our objective is to combine a logical and neural model for
enhanced link prediction. To tackle the challenge of dealing with incorrect
paths or rules generated by the logical model, we propose a semi-supervised
method to convert rules into sentences. These sentences are then subjected to
assessment and removal of incorrect rules using an NLI (Natural Language
Inference) model. Our approach to combining logical and neural models involves
first obtaining answers from both the logical and neural models. These answers
are subsequently unified using an Inference Engine module, which has been
realized through both algorithmic implementation and a novel neural model
architecture. To validate the efficacy of our model, we conducted a series of
experiments. The results demonstrate the superior performance of our model in
both link prediction metrics and the generation of more reliable explanations.
