Accelerating Innovation Through Analogy Mining
This addresses the problem of inefficient analogy mining for innovators and researchers, offering a scalable computational approach that improves over existing methods, though it is incremental in advancing structural similarity handling.
The paper tackles the challenge of finding useful analogies in large idea repositories like patents to accelerate innovation, by learning structural representations called 'problem schemas' from product descriptions using crowdsourcing and recurrent neural networks. The results show higher precision and recall in retrieving analogies compared to traditional methods, and in an ideation experiment, these analogies significantly increased people's likelihood of generating creative ideas.
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.