A workflow was developed to query and visualize biological pathways involved in sparse metabolomics data. Using knowledge from three pathway resources, directed networks between active metabolites (biomarkers) from metabolomics data can be visualized and extended with additional knowledge. The data is made interoperable by collapsing metabolites in the pathways onto single nodes using ontological approaches. This explicit ontological linking allows for precise biological interpretation of the paths. By using Neo4j and Cytoscape, the computational calculation environment for larger networks as well as advanced visualization functionality to investigate the identified subnetworks are ensured. The generic nature of this approach opens up the option to combine with other omics data sources, such as proteomics and transcriptomics.