SRTK: A Toolkit for Semantic-relevant Subgraph Retrieval

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 Second, existing approaches are knowledge-graph-dependent, resulting in outdated knowledge graphs even in recent studies.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.