System and method for hybrid question answering over knowledge graph

Abstract

Aspects of the subject disclosure may include, for example, identifying an entity of a natural language question, locating a node of a knowledge graph corresponding to the entity, and generating a candidate answer set including a group of other entities located a predetermined proximity to the node. Contextual information for the group of other entities is determined from the knowledge graph, and the natural language question and contextual information are separately encoded to obtain separate encoded vectorial representations of the natural language question and members of the candidate answer set. The encoding uses pre-trained language model embeddings obtained via a bidirectional encoder representations from transformer encoding process. The encoded vectorial representations of the question under an influence of aspects of the contextual information are scored and a member of the candidate answer set selected according to the score to obtain an answer to the original question. Other embodiments are disclosed.