Noise detection in knowledge graphs

A computer-implemented method, comprising:

• a processor;
• selecting, by a system operatively coupled to the processor, between inconsistent noise, generic noise and factually false noise as a type of noise comprised within a knowledge graph;
• autonomously classifying, by the system, employing computer-based artificial intelligence, the type of noise comprised within the knowledge graph, wherein the type of noise is generated by an information extraction process;
• and
• identifying, by the system, a second knowledge base triple comprised within the knowledge graph as the generic noise based on a presence of a common noun comprising the second knowledge base triple;
• and
• determining, by the system, the knowledge base triple comprises the common noun using a natural language processing tool to perform part-of-speech tagging.

A computer program product that autonomously classifies noise in a knowledge graph, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• select, by the processor, between a plurality of different types of noise;
• autonomously classify, by the processor, employing computer-based artificial intelligence, a type of noise from the different types of noise selected from a group consisting of inconsistent noise, generic noise, and factually false noise, wherein the type of noise is comprised within the knowledge graph, and wherein the type of noise is generated by an information extraction process;
• and
• identify, by the processor, a second knowledge base triple comprised within the knowledge graph as the generic noise based on a presence of a common noun comprising the second knowledge base triple;
• and
• determining, by the processor, the knowledge base triple comprises the common noun using a natural language processing tool to perform part-of-speech tagging.