@inproceedings{sweed-etal-2025-finding,
title = "Finding your {MUSE}: Mining Unexpected Solutions Engine",
author = "Sweed, Nir and
Hakim, Hanit and
Wolfson, Ben and
Lifshitz, Hila and
Shahaf, Dafna",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1547/",
doi = "10.18653/v1/2025.emnlp-main.1547",
pages = "30419--30434",
ISBN = "979-8-89176-332-6",
abstract = "Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research. We introduced MUSE, a novel engine to find unexpected solutions to problems. This engine consists of the inspiration graph, whose problem and solution nodes were extracted from 500K patent descriptions. For a given problem, MUSE aims to enhance users' creative problem solving by providing them with inspirations sampled from the inspiration graph. A user study indicates that participants exposed to MUSE{'}s inspirations generated more creative ideas, both in terms of absolute number (up to 19{\%} increase over participants not given inspirations) and ratio (75{\%}, compared to 49{\%} for no inspirations)."
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<abstract>Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research. We introduced MUSE, a novel engine to find unexpected solutions to problems. This engine consists of the inspiration graph, whose problem and solution nodes were extracted from 500K patent descriptions. For a given problem, MUSE aims to enhance users’ creative problem solving by providing them with inspirations sampled from the inspiration graph. A user study indicates that participants exposed to MUSE’s inspirations generated more creative ideas, both in terms of absolute number (up to 19% increase over participants not given inspirations) and ratio (75%, compared to 49% for no inspirations).</abstract>
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%0 Conference Proceedings
%T Finding your MUSE: Mining Unexpected Solutions Engine
%A Sweed, Nir
%A Hakim, Hanit
%A Wolfson, Ben
%A Lifshitz, Hila
%A Shahaf, Dafna
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F sweed-etal-2025-finding
%X Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research. We introduced MUSE, a novel engine to find unexpected solutions to problems. This engine consists of the inspiration graph, whose problem and solution nodes were extracted from 500K patent descriptions. For a given problem, MUSE aims to enhance users’ creative problem solving by providing them with inspirations sampled from the inspiration graph. A user study indicates that participants exposed to MUSE’s inspirations generated more creative ideas, both in terms of absolute number (up to 19% increase over participants not given inspirations) and ratio (75%, compared to 49% for no inspirations).
%R 10.18653/v1/2025.emnlp-main.1547
%U https://aclanthology.org/2025.emnlp-main.1547/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1547
%P 30419-30434
Markdown (Informal)
[Finding your MUSE: Mining Unexpected Solutions Engine](https://aclanthology.org/2025.emnlp-main.1547/) (Sweed et al., EMNLP 2025)
ACL
- Nir Sweed, Hanit Hakim, Ben Wolfson, Hila Lifshitz, and Dafna Shahaf. 2025. Finding your MUSE: Mining Unexpected Solutions Engine. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30419–30434, Suzhou, China. Association for Computational Linguistics.