Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

28.07.2021

Paper accepted at ISWC 2021

Nitisha Jain, Trung-Kien Tran, Mohamed H. Gad-Elrab, Daria Stepanova

Our research paper titled 'Improving Knowledge Graph Embeddings with Ontological Reasoning' has been accepted for presentation at the International Semantic Web Conference 2021 (ISWC 2021).

The paper describes our work on a reasoning based approach to generate negative samples for knowledge graph embeddings. This work was partly realized during my research internship at the Bosch Center for Artificial Intelligence (Renningen) during October 2020 - January 2021.

Abstract

Knowledge graph (KG) embedding models have emerged as powerful means for KG completion. To learn the representation of KGs, entities and relations are projected in a low-dimensional vector space so that not only existing triples in the KG are preserved but also new triples can be predicted. Embedding models might learn a good representation of the input KG, but due to the nature of machine learning approaches, they often lose the semantics of entities and relations, which might lead to nonsensical predictions. To address this issue we propose to improve the accuracy of embeddings using ontological reasoning.

More specifically, we present a novel  iterative approach ReasonKGE that identifies  dynamically via symbolic reasoning inconsistent predictions produced by a given embedding model and feeds them as negative samples for retraining this model. In order to address the scalability problem that arises when integrating ontological reasoning into the training process, we propose an advanced technique to generalize the inconsistent predictions to other semantically similar negative samples during retraining. Experimental results demonstrate the improvements in accuracy of facts produced by our method compared to the state-of-the-art.