The outcomes of host-microbiome interactions are affected by many factors such as age, gender, disease history, and ethnicity. To systematically represent and standardize, integrate, and analyze various types of host-microbiome interactions and their associated factors, the community-based Ontology of Host-Microbiome Interactions (OHMI) is developed. As one use case, we performed literature meta-analysis and identified over 100 bacteria and fungi from the gut, oral cavity, skin, and airway that are associated with six rheumatic diseases. Our ontological representation and analysis identified new scientific insights. Furthermore, we developed and applied an ontology-based “Reverse Microbiomics” bioinformatics strategy to predict microbial genes as virulence factors of rheumatoid arthritis. Another ongoing study is the systematic analysis of gastric cancer-associated host-microbiome interactions.