Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks

TL;DR

This paper presents a novel autoencoder-based method that enables neural networks to generate and align their concept-level knowledge graphs with human knowledge, improving interpretability and enabling symbolic reasoning.

Contribution

It introduces a new approach combining VSA and auxiliary tasks for end-to-end training without relying on ontologies or embeddings, directly aligning network concepts with human knowledge.

Findings

Network-generated concepts closely align with human knowledge

Method uncovers new useful concepts beyond human annotations

Enhances neural network interpretability and symbolic reasoning integration

Abstract

This paper develops an innovative method that enables neural networks to generate and utilize knowledge graphs, which describe their concept-level knowledge and optimize network parameters through alignment with human-provided knowledge. This research addresses a gap where traditionally, network-generated knowledge has been limited to applications in downstream symbolic analysis or enhancing network transparency. By integrating a novel autoencoder design with the Vector Symbolic Architecture (VSA), we have introduced auxiliary tasks that support end-to-end training. Our approach eschews traditional dependencies on ontologies or word embedding models, mining concepts from neural networks and directly aligning them with human knowledge. Experiments show that our method consistently captures network-generated concepts that align closely with human knowledge and can even uncover new, useful concepts not previously identified by humans. This plug-and-play strategy not only enhances the interpretability of neural networks but also facilitates the integration of symbolic logical reasoning within these systems.