科学学中的语义和关系空间：用于文章向量化的深度学习模型
Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation
摘要：
在过去的一个世纪里，我们观察到全球科学出版物的稳定和指数增长。大量的可用文献使得基于人工检查的领域内和领域之间的研究的整体分析是不可能的。自动技术，以支持文献综述的过程中需要找到嵌入在科学出版物的认知和社会模式。在计算机科学中，已经开发了新的工具来处理大量数据。特别是，深度学习技术开启了自动化端到端模型的可能性，以将观察结果投影到新的低维空间，其中突出显示每个观察结果的最相关信息。使用深度学习来构建科学出版物的新表示是一个不断发展但仍在兴起的研究领域。本文的目的是讨论深度学习在收集科学研究文章见解方面的潜力和局限性。我们专注于基于文章的语义和关系方面的文档级嵌入，使用自然语言处理（NLP）和图神经网络（GNN）。我们探索这些技术产生的不同结果。我们的研究结果表明，使用NLP，我们可以对文章的语义空间进行编码，而使用GNN，我们能够构建一个关系空间，其中研究社区的社会实践也被编码。
英文摘要：
Over the last century, we observe a steady and exponentially growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
