# Dual Embodied-Symbolic Concept Representations for Deep Learning

arXiv: https://arxiv.org/abs/2203.00600 · HTML: https://mlsanity.com/paper/2203.00600/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2103.04059/) (2021)
- [Bridging Knowledge Graphs to Generate Scene Graphs](https://mlsanity.com/paper/2001.02314/) (2020)
- [Multi-Modal Knowledge Graph Construction and Application: A Survey](https://mlsanity.com/paper/2202.05786/) (2022)
- [CSKG: The CommonSense Knowledge Graph](https://mlsanity.com/paper/2012.11490/) (2020)
- [A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models](https://mlsanity.com/paper/2010.15036/) (2020)
- [A Survey of Knowledge Graph Embedding and Their Applications](https://mlsanity.com/paper/2107.07842/) (2021)
- [From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information](https://mlsanity.com/paper/2005.04684/) (2020)
- [Knowledge Graph Embeddings and Explainable AI](https://mlsanity.com/paper/2004.14843/) (2020)
- [Concept-Oriented Deep Learning](https://mlsanity.com/paper/1806.01756/) (2018)
- [Concept Representation Learning with Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2112.05677/) (2021)
- [Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2202.02601/) (2022)

## Built on by (most cited first, 2 of 2)

- [Concept-Oriented Deep Learning with Large Language Models](https://mlsanity.com/paper/2306.17089/) (2023)
- [Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs](https://mlsanity.com/paper/2205.06783/) (2022)

## Abstract (arXiv)

Motivated by recent findings from cognitive neural science, we advocate the use of a dual-level model for concept representations: the embodied level consists of concept-oriented feature representations, and the symbolic level consists of concept graphs. Embodied concept representations are modality specific and exist in the form of feature vectors in a feature space. Symbolic concept representations, on the other hand, are amodal and language specific, and exist in the form of word / knowledge-graph embeddings in a concept / knowledge space. The human conceptual system comprises both embodied representations and symbolic representations, which typically interact to drive conceptual processing. As such, we further advocate the use of dual embodied-symbolic concept representations for deep learning. To demonstrate their usage and value, we discuss two important use cases: embodied-symbolic knowledge distillation for few-shot class incremental learning, and embodied-symbolic fused representation for image-text matching. Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss two important examples of such integration: scene graph generation with knowledge graph bridging, and multimodal knowledge graphs.