# Data-Driven Design-by-Analogy: State of the Art and Future Directions

arXiv: https://arxiv.org/abs/2106.01592 · HTML: https://mlsanity.com/paper/2106.01592/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 148 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis](https://mlsanity.com/paper/2103.06242/) (2021)
- [VISALOGY: Answering Visual Analogy Questions](https://mlsanity.com/paper/1510.08973/) (2015)
- [Analogy Mining for Specific Design Needs](https://mlsanity.com/paper/1712.06880/) (2017)
- [In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts](https://mlsanity.com/paper/2005.07751/) (2020)
- [Semantic Networks for Engineering Design: A Survey](https://mlsanity.com/paper/2012.07060/) (2020)
- [Design Knowledge Representation with Technology Semantic Network](https://mlsanity.com/paper/2012.15518/) (2020)
- [A Scientific Information Extraction Dataset for Nature Inspired Engineering](https://mlsanity.com/paper/2005.07753/) (2020)
- [Crowdfunding for Design Innovation: Prediction Model with Critical Factors](https://mlsanity.com/paper/2007.01404/) (2020)

## Built on by (most cited first, 18 of 18)

- [Generative Transformers for Design Concept Generation](https://mlsanity.com/paper/2211.03468/) (2022)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers](https://mlsanity.com/paper/2212.13196/) (2022)
- [Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketching](https://mlsanity.com/paper/2501.18588/) (2025)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models](https://mlsanity.com/paper/2403.13002/) (2024)
- [Large Language Models for Combinatorial Optimization of Design Structure Matrix](https://mlsanity.com/paper/2411.12571/) (2024)
- [Toward AI Assistants That Let Designers Design](https://mlsanity.com/paper/2107.13074/) (2021)
- [Toward Artificial Empathy for Human-Centered Design: A Framework](https://mlsanity.com/paper/2303.10583/) (2023)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems](https://mlsanity.com/paper/2406.19493/) (2024)
- [Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Design](https://mlsanity.com/paper/2407.11991/) (2024)
- [Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data](https://mlsanity.com/paper/2110.13624/) (2021)
- [Generative Pre-Trained Transformers for Biologically Inspired Design](https://mlsanity.com/paper/2204.09714/) (2022)
- [Beyond Input-Output: Rethinking Creativity through Design-by-Analogy in Human-AI Collaboration](https://mlsanity.com/paper/2602.09423/) (2026)
- [Large Language Models for Combinatorial Optimization of Design Structure Matrix](https://mlsanity.com/paper/2506.09749/) (2025)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)

## Abstract (arXiv)

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.