论文标题
逐步出门：通过知识渊博的检索和阅读理解，知识图完成
Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension
论文作者
论文摘要
作为许多AI应用的基石，知识图通常会面临严重的不完整问题。近年来，已经进行了许多研究自动知识图（KGC）的努力，其中大多数使用现有知识来推断新知识。但是，在我们的实验中，我们发现并非所有关系都可以通过推理获得，这会限制现有模型的性能。为了减轻这个问题，我们提出了一个基于信息检索和阅读理解的新模型，即IR4KGC。具体来说，我们预先培训了基于知识的信息检索模块，该模块可以检索与要完成的三元组相关的文档。然后，将检索的文档移交给阅读理解模块以生成预测的答案。在实验中，我们发现我们的模型可以很好地解决无法从现有知识中推断出来的关系，并在KGC数据集上获得良好的结果。
Knowledge graphs, as the cornerstone of many AI applications, usually face serious incompleteness problems. In recent years, there have been many efforts to study automatic knowledge graph completion (KGC), most of which use existing knowledge to infer new knowledge. However, in our experiments, we find that not all relations can be obtained by inference, which constrains the performance of existing models. To alleviate this problem, we propose a new model based on information retrieval and reading comprehension, namely IR4KGC. Specifically, we pre-train a knowledge-based information retrieval module that can retrieve documents related to the triples to be completed. Then, the retrieved documents are handed over to the reading comprehension module to generate the predicted answers. In experiments, we find that our model can well solve relations that cannot be inferred from existing knowledge, and achieve good results on KGC datasets.
