Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning

TL;DR

This paper proposes a loosely-coupled approach combining knowledge graph embeddings with domain-specific reasoning to improve link prediction accuracy, enabling integration of expert knowledge and surpassing existing hybrid methods.

Contribution

It introduces a flexible framework that integrates knowledge graph embeddings with reasoning, significantly enhancing prediction accuracy over traditional data-driven methods.

Findings

Up to 3x improvement in MRR accuracy over vanilla embeddings

Outperforms hybrid rule mining and reasoning solutions by up to 3.5x MRR

Demonstrates the effectiveness of combining domain knowledge with embeddings

Abstract

Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion we propose to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or entailment regimes (e.g., OWL2). In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. Our initial results show that we enhance the MRR accuracy of vanilla knowledge graph embeddings by up to 3x and outperform hybrid solutions that combine knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.