Modelling Symbolic Knowledge Using Neural Representations

Abstract

Symbolic reasoning and deep learning are two fundamentally different approaches to building AI systems, with complementary strengths and weaknesses. Despite their clear differences, however, the line between these two approaches is increasingly blurry. For instance, the neural language models which are popular in Natural Language Processing are increasingly playing the role of knowledge bases, while neural network learning strategies are being used to learn symbolic knowledge, and to develop strategies for reasoning more flexibly with such knowledge. This blurring of the boundary between symbolic and neural methods offers significant opportunities for developing systems that can combine the flexibility and inductive capabilities of neural networks with the transparency and systematic reasoning abilities of symbolic frameworks. At the same time, there are still many open questions around how such a combination can best be achieved. This paper presents an overview of recent work on the relationship between symbolic knowledge and neural representations, with a focus on the use of neural networks, and vector representations more generally, for encoding knowledge.

DEPTWEET: A typology for social media texts to detect depression severities

“…Unfortunately, these approaches fall short due to scalability. Recent approaches combine symbolic and data-driven approaches to solve this problem (Cocarascu and Toni, 2018;Faghihi et al, 2021;Schockaert and Gutiérrez-Basulto, 2022). However, we limit ourselves to data-driven approaches to keep the baseline models simple.…”