Modern metabolomics methods have been effectively used to decipher NE content . These methods are usually based on ultra-high-performance liquid chromatography coupled with data-dependant acquisition (DDA) tandem high-resolution mass spectrometry (UHPLC-HRMS 2 ) analysis, tipically followed by molecular networking (MN) and spectral annotation . Different spectral annotation tools exist for labelling unknown spectra at the structural level, such as SIRIUS/CSI:FingerID or spectral matching, or CANOPUS at the chemical class level, for example . On the other hand, the same spectral and structural datasets can also be leveraged within research projects for the discovery of bioactive compounds. Several computational approaches have been developed, such as multi-informative MN, bioactivity-based MN, or Compound Activity Mapping, which allow the integration of bioassay screening results and metabolomics data to highlight potentially active compounds in NE prior to any physical isolation processes . These computational developments led to increasing data volumes in NP research and triggered the need for advanced data analytics methods. We, therefore, recently developed a sample-centric framework to semantically enrich and align LC-MS data from many samples into a single knowledge graph, the Experimental Natural Products Knowledge Graph (ENPKG).