Ensuring the veracity of assertions is vital for building reliable and consistent knowledge graphs. A variety of automatic fact-checking approaches have been proposed over the past decade. Among these, path-based fact-checking approaches are particularly attractive due to their independence of supplementary external knowledge and their faster runtimes compared to methods reliant on external corpora or embeddings. However, the effectiveness of these approaches is fundamentally limited by the incompleteness of existing knowledge graphs, which often lack the paths necessary to support or refute assertions. To address this limitation, we propose ShallKnow, a framework that supplements the knowledge graph with shallow knowledge—automatically extracted RDF assertions from external unstructured sources—even if this additional knowledge may not always fit a well-defined ontology nor be fully verified. By appending such shallow knowledge, we enhance the graph’s coverage and increase the chances of finding relevant evidence for fact-checking. Comprehensive experiments on three widely used benchmark datasets demonstrate that integrating ShallKnow consistently and significantly enhances the performance of state-of-the-art path-based fact-checking approaches, yielding improvements of up to 0.24 in Area Under the Receiver Operating Characteristic Curve (AUROC). These results establish ShallKnow as a broadly applicable auxiliary component for improving the reliability and coverage of automatic fact-checking in knowledge graphs. Our code is open-source and can be found at https://github.com/dice-group/ShallKnow .