The scientific literature is a rich source of information for data mining
with conceptual knowledge graphs; the open science movement has enriched this
literature with complementary source code that implements scientific models. To
exploit this new resource, we construct a knowledge graph using unsupervised
learning methods to identify conceptual entities. We associate source code
entities to these natural language concepts using word embedding and clustering
techniques. Practical naming conventions for methods and functions tend to
reflect the concept(s) they implement. We take advantage of this specificity by
presenting a novel process for joint clustering text concepts that combines
word-embeddings, nonlinear dimensionality reduction, and clustering techniques
to assist in understanding, organizing, and comparing software in the open
science ecosystem. With our pipeline, we aim to assist scientists in building
on existing models in their discipline when making novel models for new
phenomena. By combining source code and conceptual information, our knowledge
graph enhances corpus-wide understanding of scientific literature.

利用无监督学习方法构建知识图谱，将自然语言概念实体与源代码实体相关联，结合词嵌入、降维和聚类技术的文本概念聚类方法，用于帮助科学家理解、组织和比较开放科学生态系统中的软件，并增强对科学文献的整体理解。