Knowledge Graph Construction (KGC) can be seen as an iterative process
starting from a high quality nucleus that is refined by knowledge extraction
approaches in a virtuous loop. Such a nucleus can be obtained from knowledge
existing in an open KG like Wikidata. However, due to the size of such generic
KGs, integrating them as a whole may entail irrelevant content and scalability
issues. We propose an analogy-based approach that starts from seed entities of
interest in a generic KG, and keeps or prunes their neighboring entities. We
evaluate our approach on Wikidata through two manually labeled datasets that
contain either domain-homogeneous or -heterogeneous seed entities. We
empirically show that our analogy-based approach outperforms LSTM, Random
Forest, SVM, and MLP, with a drastically lower number of parameters. We also
evaluate its generalization potential in a transfer learning setting. These
results advocate for the further integration of analogy-based inference in
tasks related to the KG lifecycle.

本文提出了一种基于类比推理的方法来构建知识图谱，该方法通过从感兴趣的种子实体开始，保留或修剪它们的相邻实体来逐步增加知识图谱，结果表明该方法在特定数据集和迁移学习中表现出较高的性能和泛化能力。