| CPC G06F 40/247 (2020.01) [G06F 16/313 (2019.01); G06F 16/3344 (2019.01); G06F 16/36 (2019.01); G06F 16/367 (2019.01); G06N 5/01 (2023.01)] | 20 Claims |

|
1. A processor-implemented method for mapping terms across different ontologies, the method comprising:
refining a natural language processing (NLP) model that encodes terms of a first hierarchical ontology and of a second hierarchical ontology as embeddings in a vector space in which spatial proximity between the embeddings is correlated with similarity between the associated terms, the refining including a first round of triplet loss training effective to decrease a separation between select pairs of the embeddings sampled from different ontologies that satisfy a first hierarchical relation while increasing separation between other pairs of the embeddings that do not satisfy the first hierarchical relation;
determining, from the NLP model, a stable matching scheme that matches each term in the first hierarchical ontology with a corresponding term of the second hierarchical ontology;
receiving a group of terms of the first hierarchical ontology; and
mapping, based on the stable matching scheme, each term in the group of terms of the first hierarchical ontology to its associated corresponding term of the second hierarchical ontology; and
returning the corresponding terms from the second hierarchical ontology.
|