Combine Knowledge Graph Embeddings and Reasoning

Knowledge graphs (KGs) are extensively used in many application domains, such as search engines, product recommendation, and bioinformatics. 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 the above applications. This project will investigate how to loosely-couple the data-driven power of knowledge graph embeddings with domain-specific reasoning stemming from experts or reasoning systems. 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.

The project will build upon this paper: https://arxiv.org/pdf/2202.03173 and the data used will come from a collaboration with Kansas State University.

Prerequisites: programming skills in Python; and (preferably) knowledge in about knowledge graphs and reasoning.