An Ontology−Based Deep Learning Approach for Knowledge Graph Completion with Fresh Entities
Elvira Amador−Domı́nguez‚ Patrick Hohenecker‚ Thomas Lukasiewicz‚ Daniel Manrique and Emilio Serrano
This paper introduces a new initialization method for knowledge graph (KG) embedding that can leverage ontological information in knowledge graph completion problems, such as link classification and link prediction. Although the initialization method is general and applicable to different KG embedding approaches in the literature, such as TransE or RESCAL, this paper experiments with deep learning and specifically with the neural tensor network (NTN) model. The experimental results show that the proposed method can improve link classification for a given relation by up to 15%. In a second contribution, the proposed method allows for addressing a problem not studied in the literature and introduced here as “KG completion with fresh entities”. This is the use of KG embeddings for KG completion when one or several of the entities in a triple (head, relation, tail) has not been observed in the training phase.