Cause estimation method and information processing apparatus

A non-transitory computer-readable storage medium storing therein a computer program that causes a computer to execute a process comprising:

• receiving an input of a sentence indicating a first phenomenon whose cause is to be estimated;
• selecting one or a plurality of first nodes indicating a second phenomenon that is similar to the first phenomenon, out of a plurality of nodes in a knowledge graph including the plurality of nodes which indicate a plurality of phenomena and edges indicating causal relationships between the plurality of phenomena;
• generating a sub-knowledge graph including a first node and a second node by tracing from the first node as a starting point to the second node at an end on a cause side of the causal relationship indicated by an edge;
• determining a confidence level that a third phenomenon indicated by the second node included in the sub-knowledge graph is a cause of the first phenomenon based on a similarity of the first node included in the sub-knowledge graph to the first phenomenon;
• and
• determining, based on the confidence level of the sub-knowledge graph, whether to include the third phenomenon indicated by the second node included in the sub-knowledge graph in cause candidates of the first phenomenon.

An information processing apparatus comprising:

• a memory;
• and
• a processor coupled to the memory and the processor configured to:
• receive an input of a sentence indicating a first phenomenon whose cause is to be estimated;
• select one or a plurality of first nodes indicating a second phenomenon that is similar to the first phenomenon, out of a plurality of nodes in a knowledge graph including the plurality of nodes which indicate a plurality of phenomena and edges indicating causal relationships between the plurality of phenomena;
• generate a sub-knowledge graph including a first node and a second node by tracing from the first node as a starting point to the second node at an end on a cause side of the causal relationship indicated by an edge;
• determine a confidence level that a third phenomenon indicated by the second node included in the sub-knowledge graph is a cause of the first phenomenon based on a similarity of the first node included in the sub-knowledge graph to the first phenomenon;
• and
• determine, based on the confidence level of the sub-knowledge graph, whether to include the third phenomenon indicated by the second node included in the sub-knowledge graph in cause candidates of the first phenomenon.