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.

翻译：知识图谱补全是一项基于图谱中已有信息填补缺失三元组的任务。在现有研究中，基于文本的方法通过利用三元组的文本描述来完成该任务。然而，这种建模方法可能遭遇局限性，尤其是在描述无法准确且充分表达预期含义时。为克服这些挑战，我们提出通过两种额外机制来增强数据。首先，我们利用ChatGPT作为外部知识库生成连贯的描述，以弥合查询与答案之间的语义鸿沟。其次，我们利用逆关系构建对称图，从而为链接预测提供额外标注和补充信息。该方法能为实体间关系提供更深层次的洞察。通过上述努力，我们观察到知识图谱补全效果显著提升——这些机制增强了可用数据的丰富性与多样性，从而获得更精确的结果。