Abstract
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.
Original language | English |
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Title of host publication | KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
Publisher | Association for Computing Machinery |
Pages | 235-243 |
Number of pages | 9 |
ISBN (Electronic) | 9781450348874 |
DOIs | |
State | Published - 13 Aug 2017 |
Event | 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada Duration: 13 Aug 2017 → 17 Aug 2017 |
Publication series
Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
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Volume | Part F129685 |
Conference
Conference | 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 |
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Country/Territory | Canada |
City | Halifax |
Period | 13/08/17 → 17/08/17 |
Bibliographical note
Publisher Copyright:© 2017 ACM.
Keywords
- Computational analogy
- Creativity
- Innovation
- Product dimensions
- Text embedding
- Text mining