KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with
  Inverse Transformation
Haotian Li Lingzhi Wang Yuliang Wei Richard Yi Da Xu Bailing Wang
Knowledge graph completion is a task that revolves around filling in missing
triples based on the information available in a knowledge graph. Among the
current studies, text-based methods complete the task by utilizing textual
descriptions of triples. However, this modeling approach may encounter
limitations, particularly when the description fails to accurately and
adequately express the intended meaning. To overcome these challenges, we
propose the augmentation of data through two additional mechanisms. Firstly, we
employ ChatGPT as an external knowledge base to generate coherent descriptions
to bridge the semantic gap between the queries and answers. Secondly, we
leverage inverse relations to create a symmetric graph, thereby creating extra
labeling and providing supplementary information for link prediction. This
approach offers additional insights into the relationships between entities.
Through these efforts, we have observed significant improvements in knowledge
graph completion, as these mechanisms enhance the richness and diversity of the
available data, leading to more accurate results.