Opening Knowledge Gaps Drives Scientific Progress

• URL: http://arxiv.org/abs/2509.21899v1
• Date: Fri, 26 Sep 2025 05:33:10 GMT
• Title: Opening Knowledge Gaps Drives Scientific Progress
• Abstract summary: Gap-opening papers are more likely to rank among the most highly cited works.<n>Papers that introduce novel combinations without opening gaps are not more likely to rank in the top 1% for citation counts.<n>Our findings suggest that gap-opening papers are more disruptive, highlighting their generative role in stimulating new directions for scientific inquiry.
• Score: 2.6067353186988305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge production is often viewed as an endogenous process in which discovery arises through the recombination of existing theories, findings, and concepts. Yet given the vast space of potential recombinations, not all are equally valuable, and identifying those that may prove most generative remains challenging. We argue that a crucial form of recombination occurs when linking concepts creates knowledge gaps-empty regions in the conceptual landscape that focus scientific attention on proximal, unexplored connections and signal promising directions for future research. Using computational topology, we develop a method to systematically identify knowledge gaps in science at scale. Applying this approach to millions of articles from Microsoft Academic Graph (n = 34,363,623) over a 120-year period (1900-2020), we uncover papers that create topological gaps in concept networks, tracking how these gap-opening works reshape the scientific knowledge landscape. Our results indicate that gap-opening papers are more likely to rank among the most highly cited works (top 1-20%) compared with papers that do not introduce novel concept pairings. In contrast, papers that introduce novel combinations without opening gaps are not more likely to rank in the top 1% for citation counts, and are even less likely than baseline papers to appear in the top 5% to 20%. Our findings also suggest that gap-opening papers are more disruptive, highlighting their generative role in stimulating new directions for scientific inquiry.

Related papers

• SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research [64.97707567378497]
  We introduce SciAtlas, a large-scale, multi-disciplinary, heterogeneous academic resource knowledge graph.<n>By integrating over 43M papers from 26 disciplines, and a total of 157M entities and 3B triplets, SciAtlas provides a structured topological cognitive substrate.<n>We develop a neuro-symbolic retrieval algorithm featuring tri-path collaborative recall and graph reranking, achieving a seamless transition from simple semantic matching to deterministic association discovery.
  arXiv  Detail & Related papers  (2026-05-20T16:03:29Z)
• Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists [35.81584849462589]
  Intern-Atlas is a methodological evolution graph that automatically identifies method-level entities.<n>Built from 1,030,314 papers spanning AI conferences, journals, and arXiv preprints, the resulting graph comprises 9,410,201 semantically typed edges.
  arXiv  Detail & Related papers  (2026-04-30T17:44:55Z)
• Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies [9.063254770910948]
  This study proposes a multi-agent iterative planning search strategy inspired by com-binatorial innovation theory.<n>The framework combines iterative knowledge search with an LLM-based multi-agent system to generate, evaluate, and re-fine research ideas.<n>Experiments in the natural language processing domain show that the proposed method outperforms state-of-the-art base-lines in both diversity and novelty.
  arXiv  Detail & Related papers  (2026-04-22T13:31:12Z)
• Alien Science: Sampling Coherent but Cognitively Unavailable Research Directions from Idea Atoms [53.907293349123506]
  Large language models often fail at producing ideas that are both coherent and non-obvious to the current community.<n>We formalize this gap through cognitive availability, the likelihood that a research direction would be naturally proposed by a typical researcher.<n>We learn two complementary models: a coherence model that scores whether a set of atoms constitutes a viable direction, and an availability model that scores how likely that direction is to be generated.
  arXiv  Detail & Related papers  (2026-03-01T13:05:19Z)
• Accelerating Scientific Research with Gemini: Case Studies and Common Techniques [105.15622072347811]
  Large language models (LLMs) have opened new avenues for accelerating scientific research.<n>We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models.
  arXiv  Detail & Related papers  (2026-02-03T18:56:17Z)
• Higher-Order Knowledge Representations for Agentic Scientific Reasoning [1.1458853556386797]
  We introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships.<n> Applied to a corpus of 1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges.<n>We further demonstrate that equipping agentic systems with hypergraph tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts.
  arXiv  Detail & Related papers  (2026-01-08T12:25:37Z)
• THE-Tree: Can Tracing Historical Evolution Enhance Scientific Verification and Reasoning? [16.91455372359864]
  We introduce textbfTHE-Tree (textbfTechnology textbfHistory textbfEvolution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature.
  arXiv  Detail & Related papers  (2025-06-26T20:44:51Z)
• Triadic Novelty: A Typology and Measurement Framework for Recognizing Novel Contributions in Science [0.8249694498830561]
  Existing metrics conflate novelty with popularity, privileging ideas that fit existing paradigms over those that challenge them.<n>This study develops a theory-driven framework to better understand how different types of novelty emerge, take hold, and receive recognition.
  arXiv  Detail & Related papers  (2025-06-21T23:09:04Z)
• Open-world machine learning: A review and new outlooks [117.33922838201993]
  Article presents a holistic view of open-world machine learning.<n>It investigates unknown rejection, novelty discovery, and continual learning.<n>It aims to help researchers build more powerful AI systems in their respective fields.
  arXiv  Detail & Related papers  (2024-03-04T06:25:26Z)
• Large Language Models for Automated Open-domain Scientific Hypotheses Discovery [50.40483334131271]
  This work proposes the first dataset for social science academic hypotheses discovery. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi- module framework is developed for the task, including three different feedback mechanisms to boost performance.
  arXiv  Detail & Related papers  (2023-09-06T05:19:41Z)
• Exploring and Verbalizing Academic Ideas by Concept Co-occurrence [42.16213986603552]
  This study devises a framework based on concept co-occurrence for academic idea inspiration. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. We generate a description of an idea based on a new data structure called co-occurrence citation quintuple.
  arXiv  Detail & Related papers  (2023-06-04T07:01:30Z)
• Novel Class Discovery without Forgetting [72.52222295216062]
  We identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting. We propose a machine learning model to incrementally discover novel categories of instances from unlabeled data. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery.
  arXiv  Detail & Related papers  (2022-07-21T17:54:36Z)
• Remote Collaboration Fuses Fewer Breakthrough Ideas [40.14431045018876]
  We show that researchers in remote teams are consistently less likely to make breakthrough discoveries. We find that among distributed team members, collaboration centers on late-stage, technical tasks involving more codified knowledge.
  arXiv  Detail & Related papers  (2022-06-04T02:19:25Z)
• Embedding Knowledge for Document Summarization: A Survey [66.76415502727802]
  Previous works proved that knowledge-embedded document summarizers excel at generating superior digests. We propose novel to recapitulate knowledge and knowledge embeddings under the document summarization view.
  arXiv  Detail & Related papers  (2022-04-24T04:36:07Z)
• Dimensions of Commonsense Knowledge [60.49243784752026]
  We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions, each abstracting over more specific relations found in sources.
  arXiv  Detail & Related papers  (2021-01-12T17:52:39Z)
• Understanding the wiring evolution in differentiable neural architecture search [114.31723873105082]
  Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. We study the underlying mechanism of several existing differentiable NAS frameworks.
  arXiv  Detail & Related papers  (2020-09-02T18:08:34Z)
• High-Precision Extraction of Emerging Concepts from Scientific Literature [29.56863792319201]
  We present an unsupervised concept extraction method for scientific literature. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%.
  arXiv  Detail & Related papers  (2020-06-11T23:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.