However, the feature alignment stage is problematic for extended UHPLC-MS 2 metabolomics research projects encompassing a large number of samples. First, since metabolite profiling data are typically recorded in independent batches, the resulting data are prone to batch effects due to the variation in both LC and MS dimensions, thus preventing a proper alignment . Secondly, the inclusion of novel samples to previously analyzed datasets requires the recomputation of alignment and post-alignment processing steps such as structural annotation or FBMN . This drawback is particularly problematic for large datasets (hundreds to thousands of samples), for which these analyses require a large amount of time and computational resources. Such classical approaches -hereafter qualified as dataset-centric -result in the compartmentalization of data and information into hermetic project-related silos, triggering a need for new methods to exploit the data. To this end, we recently developed the MEMO approach to compare chemodiverse samples based on their MS 2 fingerprints without relying on an RT-based alignment . In this work, we push this concept further and propose shifting from a dataset-centric to a sample-centric approach, enabling alignment not only through spectral data but also via related chemical information or any relevant metadata. Each sample is considered individually for taxonomic metadata standardization, feature detection, structural annotation, and FBMN. The resulting standardized data and information from all considered samples are then integrated into a single knowledge graph (KG). This sample-centric approach, implemented in the Experimental Natural Products Knowledge Graph (ENPKG) framework, is described hereafter.