Knowledge-driven and self-supervised system for question-answering

A data processing system comprising:

• at least one non-transitory computer readable medium including at least a neuro-symbolic framework, the neuro-symbolic framework including computer readable data;
• a processing system including at least one processor that is operably connected to the at least one non-transitory computer readable medium, the processor being configured to execute the computer readable data to implement a method that includes:
• obtaining a current data structure from a global knowledge graph that includes a combination of various knowledge graphs, the current data structure being associated with one knowledge graph of the global knowledge graph and including a current head element, a current relationship element, and a current tail element;
• obtaining a sentence corresponding to the current data structure;
• generating a question by removing the current tail element from the sentence;
• generating a correct answer to the question, the correct answer including the current tail element;
• extracting a pool of data structures from the global knowledge graph based on a set of distractor criteria, each extracted data structure having a head element with no common keywords with the current head element and a relationship element that is the current relationship element, the pool of data structures including at least one extracted data structure that is associated with another knowledge graph of the global knowledge graph;
• extracting tail elements from the pool of data structures to create a pool of distractor candidates;
• selecting a set of distractors from the pool of distractor candidates;
• creating a query task that includes the question and a set of response options, the set of response options including the correct answer and the set of distractors;
• creating a training set that includes at least the query task;
• and
• training a machine learning system with the training set, wherein the machine learning system is configured to receive the query task and respond to the question with a predicted answer that is selected from among the set of response options.

A computer product comprising at least one non-transitory computer readable storage device that includes computer-readable data, which when executed by one or more processors, is operable to cause the one or more processors to implement a method that comprises:

• obtaining a current data structure from a global knowledge graph that includes a combination of various knowledge graphs, the current data structure being associated with one knowledge graph of the global knowledge graph and including a current head element, a current relationship element, and a current tail element;
• obtaining a sentence corresponding to the current data structure;
• generating a question by removing the current tail element from the sentence;
• generating a correct answer to the question, the correct answer including the current tail element;
• extracting a pool of data structures from the global knowledge graph based on a set of distractor criteria, each extracted data structure having a head element with no common keywords with the current head element and a relationship element that is the current relationship element, the pool of data structures including at least one extracted data structure that is associated with another knowledge graph of the global knowledge graph;
• extracting tail elements from the pool of data structures to create a pool of distractor candidates;
• selecting a set of distractors from the pool of distractor candidates;
• creating a query task that includes the question and a set of response options, the set of response options including the correct answer and the set of distractors;
• creating a training set that includes at least the query task;
• and
• training a machine learning system with the training set, wherein the machine learning system is configured to receive the query task and respond to the question with a predicted answer that is selected from among the set of response options.