Modular Neuro-Symbolic Knowledge Graph Completion

Knowledge graph completion (a.k.a. link prediction), i.e., the task of inferring missing edges in knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. State-of-the-art approaches include knowledge graph embeddings and rule mining which are data-driven and, thus, solely based on the information contained in the input knowledge graph. This leads to unsatisfactory prediction results and ignores
domain expertise making such solutions inefficient for domains such as healthcare and bioinformatics. To enhance the accuracy of knowledge graph completion we propose Poderoso, a modular neuro-symbolic framework that loosely integrates the data-driven power of knowledge graph embeddings with rule-based reasoning. Poderoso not only enhances the prediction accuracy with domain knowledge via rules stemming from experts but also allows users to plug their own knowledge graph embedding models and reasoning engines. In our preliminary results we show that Poderoso enhances the MRR accuracy of vanilla knowledge graph embeddings and outperforms hybrid solutions that combine knowledge graph embeddings with rule mining. We also discuss how Poderoso can be used in bionformatics, in particular how it can advance research in bacteriophage therapy.