Uncertain Ontology Embeddings
Khaoula Boutouhami, Guilin Qi, Qiu Ji, Jiatao Zhang, Huan Gao · 2020
Ontologies are an expressive power used as a crucial element to enrich knowledge graphs by their rich data and ontological concepts that are often necessary for logical inference. Recently, ontology embedding has gained increasing attention in real-world ontologies for its ability to learn sub-sumption axioms, unary and binary assertions in these ontologies, while maintaining some syntactic, semantic properties and deductive closures. In general, learning methods assume an ontology to be complete while there are different situations in which it can be excepted to be uncertain, for example when they are provided by human annotators in classification, or even missing. However, embedding such uncertain ontologies is typically ignored. In this paper, we propose a novel embedding approach, UOE, which can embed uncertain lightweight ontology encoded in DL-lite in order to better capture the relationships between the uncertainty and the ontologies in the semantic space. Experimental results show that our model can robustly learn representations of uncertain ontologies when evaluated on publicly available datasets used on machine learning benchmarks.