Learning ontology axioms over knowledge graphs via representation learning
Leyuan Zhao, Xiaowang Zhang, Kewen Wang, Zhiyong Feng, Zhe Wang · Griffith Research Online · 2019
This presents a representation learning model called SetE by modeling a predicate into a subspace in a semantic space where entities are vectors. Within SetE, a type as unary predicate is encoded as a set of vectors and a relation as binary predicate is encoded as a set of pairs of vectors. A new approach is proposed to compute the subsumption of predicates in a semantic space by employing linear programming methods to determine whether entities of a type belong to a sup-type and thus an algorithm for learning OWL axioms is developed. Experiments on real datasets show that SetE can efficiently learn various forms of axioms with high quality.