Semantic analysis based on knowledge graphs requires a relevant subgraph of a reasonable size. Existing approaches have three issues that impede the integration of such subgraphs. First, there is no off-the-shelf framework for semantic-relevant subgraph retrieval. Second, existing approaches are knowledge-graph-dependent, resulting in outdated knowledge graphs even in recent studies. Third, existing approaches are flawed either in entity linking or path expansion, which often results in huge subgraphs. In this paper, we present SRTK, a user-friendly toolkit for semantic-relevant subgraph retrieval from large-scale knowledge graphs. SRTK is the first toolkit that streamlines the entire lifecycle of subgraph retrieval, from development (preprocessing, training, and evaluation) to applications (entity linking, retrieving and visualizing). Moreover, It supports multiple popular knowledge graphs by defining unified interfaces across different knowledge graphs. Additionally, it ships with a state-of-the-art subgraph retrieval algorithm out of the box. We evaluate the toolkit on Wikidata and Freebase and demonstrate its ability to retrieve semantically relevant subgraphs for a given natural query.

翻译：基于知识图谱的语义分析需要获取规模合理的相关子图。现有方法在整合此类子图时存在三个问题：首先，缺乏现成的语义相关子图检索框架；其次，现有方法过度依赖特定知识图谱，导致即便在最新研究中仍使用过时图谱；第三，现有方法在实体链接或路径扩展环节存在缺陷，常导致子图规模过于庞大。本文提出SRTK——一个从大规模知识图谱中检索语义相关子图的用户友好型工具包。SRTK是首个能够覆盖子图检索全生命周期的工具包，从预处理、训练、评估等开发环节到实体链接、检索、可视化等应用阶段均提供支持。该工具包通过定义跨知识图谱的统一接口，兼容多种主流知识图谱，并内置了当前最先进的子图检索算法。我们在Wikidata和Freebase上对该工具包进行了评估，证明其能够针对给定自然语言查询，有效检索出语义相关的子图。