title: CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature
summary: 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
paper_authors._id: 6836b63be36a95ffd7c9743f
paper_authors.name: Noy Sternlicht
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:55:29.488Z
paper_authors._id: 63e8914accae1fe5c6176a55
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63e8914accae1fe5c6176a55/_gDjcqGKWKoSZrHSEIlAw.jpeg
paper_authors.fullname: Noy Sternlicht
paper_authors.type: user
paper_authors.user: noystl
paper_authors._id: 6836b63be36a95ffd7c97440
paper_authors.name: Tom Hope
paper_title: CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: LLM-based extraction model scientific hypothesis generation model