Uncertain ontology-aware knowledge graph embeddings

Abstract

Much attention has recently been given to knowledge graphs embedding by exploiting latent and semantic relations among entities and incorporating the structured knowledge they contain into machine learning. Most of the existing graph embedding models can only encode a simple model of the data, while few models are designed for ontology rich knowledge graphs. Furthermore, many automated knowledge construction tools produce modern knowledge graphs with rich semantics and uncertainty. However, there is no graph embedding model which includes uncertain ontological information into graph embedding models. In this paper, we propose a novel embedding model UOKGE (Uncertain Ontology-aware Knowledge Graph Embeddings), which learns embeddings of entities, classes, and properties on uncertain ontology-aware knowledge graphs according to confidence scores. The proposed method preserves both structures and uncertainty of knowledge in the embedding space. Specifically, UOKGE encodes each entity in a knowledge graph as a point of n-dimensional vector, each class as a n-sphere and each property as 2n-sphere in the same semantic space. This representation allows for the natural expression of uncertain ontological triples. The preliminary experimental results show that UOKGE can robustly learn representations of uncertain ontology-aware knowledge graphs when evaluated on a benchmark dataset.