Abstract: The present disclosure provides a neural document embedding based ontology mapping. Conventional methods that map ontology concepts across domains/species extensively take help of bridging ontologies. Initially the system receives a Human Phenotype (HP) Identification number (ID) pertaining to a phenotype. A first HP ID vector is computed from the HP ID using a trained word2vec model. A second HP ID vector is computed from the HP ID using a trained Doc2vec model. An average HP ID vector is computed based on the first HP ID vector and the second HP ID vector. A plurality of cosine similarity scores are computed based on a comparison between the average HP ID vector and a plurality of average MP ID vectors. The plurality of MP IDs are sorted based on the plurality of cosine similarity scores. The plurality of MP IDs corresponding to the HP ID are selected based on a selection threshold.