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Scaling Up Innovation through Analogy Mining

Project description

Boosting discoveries through automation of analogy-finding

Analogies are often used in science. Many important discoveries in history were made through analogy findings. The analogy is the ability to find a deep structural rule and apply it in different fields. But today, the amount of available data and different structural patterns, with the support of AI and machine learning, increase dramatically the possibility of findings. The EU-funded SIAM project aims to create a mechanism for automation of the analogy-finding process by combining human innovation and machine information processing. It will employ AI in analogy finding, selecting and applying. The project will prepare tools to compare analogies and find similarities, with a view to creating algorithms based on common sense and abstraction to develop novel tools that will accelerate innovation and discovery.


"Many world-changing breakthroughs in science and technology were enabled by analogical transfer, as ideas from one domain were used to solve a problem in another. Observing water led the Greek philosopher Chrysippus to speculate that sound was a wave phenomenon; an analogy to twisting a cardboard box allowed the Wright brothers to design a steerable aircraft. Despite its value for innovation, very little progress has been made towards automating the process of analogy-finding in real-world settings, and the problem has maintained a longstanding status as a ""holy grail"" in artificial intelligence (AI).

The goal of this proposal is to tackle head-on this important problem and develop principled tools for automatically discovering analogies in large, unstructured, natural-language datasets such as patents and scientific papers. Such tools could revolutionize a variety of fields, allowing scientists and inventors to retrieve useful content based on deep structural similarity rather than simple keywords. The explosion of data available online, coupled with novel machine learning and crowdsourcing techniques, creates an unprecedented opportunity to develop novel methods to accelerate innovation and discovery.

My approach explores the multiple roles AI and machine learning can play in the analogical innovation pipeline. This research will focus on the three core components of the pipeline -- (1) developing representations and similarity metrics to facilitate comparison between potential analogs, (2) imbuing the algorithms with commonsense knowledge and abstraction capabilities, and (3) guiding the adaptation of the discovered analogies to solve the original problem. For each component, the proposal demonstrates how recent advances suggest effective approaches, and describes our concrete preliminary results and ideas to serve as starting points and indicate the feasibility of this challenging project."



Net EU contribution
€ 1 373 057,00
Edmond j safra campus givat ram
91904 Jerusalem

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Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Beneficiaries (1)