Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks
This addresses the gap in integrating symbolic reasoning into neural networks for enhanced interpretability, though it appears incremental as it builds on existing concepts like autoencoders and VSA.
The paper tackles the problem of aligning knowledge graphs generated by neural networks with human-provided knowledge, resulting in a method that consistently captures network-generated concepts closely aligned with human knowledge and uncovers new useful concepts.
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.