Surprisingness - A Novel Objective Interestingness Measure in Hypergraph Pattern Mining from Knowledge Graphs for Common Sense Learning

Abstract

Pattern mining usually results in huge amounts of patterns, among which only small percentages are interesting. In this paper, Surprisingness (including Surpringness_I and Surpringness_II) is proposed as an innovative objective multivariate interestingness measure for automatically identifying interesting patterns from a large quantity of patterns. Surprisingness is applicable in unstructured or semi-structured, multi-domain or mixed-domain data compared to existing measures. An experiment has been conducted enabling unsupervised learning of common sense, interesting patterns and exceptions from a knowledge graph database built from Wikipedia 1 extracted data (represented as directed labeled hypergraphs), using Surpringness.