A Neuro-Symbolic Explainable Machine Learning Model Using Knowledge Graphs

Motivation

Despite the popularity and recent advances in Artificial Intelligence (AI) systems boosted by machine learning (ML) methods, most of the existing models fall short on their ability to explain their reasoning process, i.e., in providing human-like justifications for the reasoning behind a certain AI task. This functionality of "being interpretable" is a fundamental requirement for the adoption and uptake of AI systems in real-world scenarios, as users need to trust and understand the approximations and inferences done by the system.

The application of AI in contexts with high social and economic impact (as in health care and legal settings) will require the evolution of black-box AI models in the direction of systems which can justify, explain and dialogue with their end-users about the underlying reasoning process, providing transparent human-interpretable outputs.

Approach

This project aims at developing a neuro-symbolic explainable Machine Learning model using Knowledge graphs. The goal is to support the construction of complex AI systems for addressing tasks such as Question Answering and Text Entailment, which can output meaningful human-like explanations in addition to the expected output.

Research Questions

The project targets the following research questions:

1. Can knowledge graphs (definitional, fact-based, discourse-level) be used in conjunction to differential(neuro) inductive logic programming (the neuro-symbolic approach) to support explainable machine learning?
2. Which set of quantitative measures can be used to evaluate explainable machine learning systems?

Novel Engineering Content

The project will articulate for the first-time the connection between knowledge graphs, which represents large-scale background knowledge and differential inductive logic programming.

EPSRC Research Areas

Natural Language Processing, Machine Learning