A Graph-Path Counting Approach for Learning Head Output Connected Relations

摘要

Concept discovery is a multi-relational data mining task where the problem is inducing definitions of a relation in terms of other relations. In this paper, we propose a graph-based concept discovery system to learn definitions of head output connected relations. It inputs the data in relational format, converts it into graphs, and induces concept definitions from graphs' paths. The proposed method can handle n-ary relations and induce recursive concept definitions. Path frequencies are used to calculate the quality of the induced concept descriptors. The experimental results show that results obtained are comparable to those reported in literature in terms of running time and coverage; and is superior over some methods as it can induce shorter concept descriptors with the same coverage.