CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature
A hallmark of human innovation is the process of recombination -- creating
original ideas by integrating elements of existing mechanisms and concepts. In
this work, we automatically mine the scientific literature and build CHIMERA: a
large-scale knowledge base (KB) of recombination examples. CHIMERA can be used
to empirically explore at scale how scientists recombine concepts and take
inspiration from different areas, or to train supervised machine learning
models that learn to predict new creative cross-domain directions. To build
this KB, we present a novel information extraction task of extracting
recombination from scientific paper abstracts, collect a high-quality corpus of
hundreds of manually annotated abstracts, and use it to train an LLM-based
extraction model. The model is applied to a large corpus of papers in the AI
domain, yielding a KB of over 28K recombination examples. We analyze CHIMERA to
explore the properties of recombination in different subareas of AI. Finally,
we train a scientific hypothesis generation model using the KB, which predicts
new recombination directions that real-world researchers find inspiring. Our
data and code are available at https://github.cs.huji.ac.il/tomhope-lab/CHIMERA
6836b63be36a95ffd7c9743f
Noy Sternlicht
claimed_verified
2025-05-28T08:55:29.488Z
63e8914accae1fe5c6176a55
https://cdn-avatars.huggingface.co/v1/production/uploads/63e8914accae1fe5c6176a55/_gDjcqGKWKoSZrHSEIlAw.jpeg
Noy Sternlicht
user
noystl
6836b63be36a95ffd7c97440
Tom Hope
CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature
A hallmark of human innovation is the process of recombination -- creating
original ideas by integrating elements of existing mechanisms and concepts. In
this work, we automatically mine the scientific literature and build CHIMERA: a
large-scale knowledge base (KB) of recombination examples. CHIMERA can be used
to empirically explore at scale how scientists recombine concepts and take
inspiration from different areas, or to train supervised machine learning
models that learn to predict new creative cross-domain directions. To build
this KB, we present a novel information extraction task of extracting
recombination from scientific paper abstracts, collect a high-quality corpus of
hundreds of manually annotated abstracts, and use it to train an LLM-based
extraction model. The model is applied to a large corpus of papers in the AI
domain, yielding a KB of over 28K recombination examples. We analyze CHIMERA to
explore the properties of recombination in different subareas of AI. Finally,
we train a scientific hypothesis generation model using the KB, which predicts
new recombination directions that real-world researchers find inspiring. Our
data and code are available at https://github.cs.huji.ac.il/tomhope-lab/CHIMERA
A large-scale knowledge base of recombination examples is built from scientific paper abstracts using an LLM-based extraction model to analyze and inspire new creative directions in AI.
LLM-based extraction model scientific hypothesis generation model