Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas
Yuexi Shen, Minqian Liu, Dawei Zhou, Lifu Huang

TL;DR
This paper introduces the Ideation Space, a structured, multi-dimensional representation of scientific ideas that improves literature retrieval and novelty assessment, thereby accelerating scientific discovery.
Contribution
It proposes a novel decomposed, contrastively trained representation of scientific knowledge and a hierarchical retrieval and novelty assessment framework, addressing limitations of existing embedding and LLM approaches.
Findings
Achieves 16.7% improvement in Recall@30 over baselines
Attains 0.643 Hit Rate@30 in ideation transition retrieval
Correlates at 0.37 with expert judgments in novelty assessment
Abstract
Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained literature retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing to provide discriminative novelty assessment. To tackle these challenges, we introduce the Ideation Space, a structured representation that decomposes scientific knowledge into three distinct dimensions, i.e., research problem, methodology, and core findings, each learned through contrastive training. This framework enables principled…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Advanced Graph Neural Networks · Advanced Text Analysis Techniques
