Zero-shot entity linking based on symbolic information

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:

• obtain a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph;
• train a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph;
• and
• map an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

A system comprising:

• a memory configured to store program instructions;
• a processor operatively coupled to the memory to execute the program instructions to:
• obtain a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph;
• train a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph;
• and
• map an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.