# Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

arXiv: https://arxiv.org/abs/2102.09761 · HTML: https://mlsanity.com/paper/2102.09761/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 105 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017)
- [Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts](https://mlsanity.com/paper/1808.06161/) (2018)
- [A Multi-task Approach for Named Entity Recognition in Social Media Data](https://mlsanity.com/paper/1906.04135/) (2019)
- [Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction](https://mlsanity.com/paper/1909.07065/) (2019)
- [Analogy Mining for Specific Design Needs](https://mlsanity.com/paper/1712.06880/) (2017)
- [Physical Principles for Scalable Neural Recording](https://mlsanity.com/paper/1306.5709/) (2013)

## Built on by (most cited first, 7 of 7)

- [Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation](https://mlsanity.com/paper/2312.02439/) (2023)
- [Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation](https://mlsanity.com/paper/2409.14634/) (2024)
- [Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps](https://mlsanity.com/paper/2410.10370/) (2024)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
- [GuessBench: Sensemaking Multimodal Creativity in the Wild](https://mlsanity.com/paper/2506.00814/) (2025)
- [Finding your MUSE: Mining Unexpected Solutions Engine](https://mlsanity.com/paper/2509.05072/) (2025)
- [RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature](https://mlsanity.com/paper/2608.27394/) (2026)

## Abstract (arXiv)

Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.