Accelerating innovation through analogy mining

Description

The availability of large idea repositories (e.g., patents) could significantly accelerate innovation and discovery by providing people inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy real-world repositories remains a persistent challenge for both humans and computers. Previous approaches include costly manually created databases that do not scale or machine learning similarity metrics that struggle to account for structural similarity, which is central to analogy.

Dafna Shahaf explores the viability and value of learning simple structural representations and presents an algorithm that automatically discovers analogies in unstructured data. The approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. Dafna demonstrates that these learned vectors allow us to find analogies with higher precision and recall than traditional methods. In an ideation experiment, analogies retrieved by these models significantly increased people’s likelihood of generating creative ideas.