Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension
This addresses the limitation of inference-based methods in knowledge graph completion, particularly for relations not derivable from existing knowledge.
The paper tackles the problem of knowledge graph incompleteness by proposing IR4KGC, a model that uses information retrieval and reading comprehension to complete triples, achieving good results on KGC datasets.
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.