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一种用于科学概念层次结构的生成模型。

A generative model for scientific concept hierarchies.

机构信息

Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States of America.

School of Information, University of Michigan, Ann Arbor, MI, United States of America.

出版信息

PLoS One. 2018 Feb 23;13(2):e0193331. doi: 10.1371/journal.pone.0193331. eCollection 2018.

DOI:10.1371/journal.pone.0193331
PMID:29474409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5825074/
Abstract

In many scientific disciplines, each new 'product' of research (method, finding, artifact, etc.) is often built upon previous findings-leading to extension and branching of scientific concepts over time. We aim to understand the evolution of scientific concepts by placing them in phylogenetic hierarchies where scientific keyphrases from a large, longitudinal academic corpora are used as a proxy of scientific concepts. These hierarchies exhibit various important properties, including power-law degree distribution, power-law component size distribution, existence of a giant component and less probability of extending an older concept. We present a generative model based on preferential attachment to simulate the graphical and temporal properties of these hierarchies which helps us understand the underlying process behind scientific concept evolution and may be useful in simulating and predicting scientific evolution.

摘要

在许多科学学科中，每一项新的研究“成果”（方法、发现、人工制品等）通常都是在前人的发现基础上构建的，从而导致科学概念随着时间的推移而扩展和分支。我们的目标是通过将科学概念置于系统发生层次结构中来理解它们的演变，其中使用来自大型纵向学术语料库的科学关键词作为科学概念的代理。这些层次结构表现出各种重要的性质，包括幂律度分布、幂律分量大小分布、存在巨型分量和较少扩展旧概念的可能性。我们提出了一种基于优先连接的生成模型来模拟这些层次结构的图形和时间性质，这有助于我们理解科学概念演变背后的潜在过程，并且可能有助于模拟和预测科学演变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/8b7c870962de/pone.0193331.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/f0ae62078e1b/pone.0193331.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/872a287d01b6/pone.0193331.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/c0c6ab7b8707/pone.0193331.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/ca509281e08c/pone.0193331.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/3150679c0c80/pone.0193331.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/07ee4484f48f/pone.0193331.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/8b7c870962de/pone.0193331.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/f0ae62078e1b/pone.0193331.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/20a924ab48d6/pone.0193331.g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/872a287d01b6/pone.0193331.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/c0c6ab7b8707/pone.0193331.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/ca509281e08c/pone.0193331.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/3150679c0c80/pone.0193331.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/07ee4484f48f/pone.0193331.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c7c/5825074/8b7c870962de/pone.0193331.g009.jpg

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