论文标题
从文本资源中自动构建算法知识图的方法
An Approach for Automatic Construction of an Algorithmic Knowledge Graph from Textual Resources
论文作者
论文摘要
各个研究领域都有巨大的增长。这种发展伴随着新问题。为了有效地以优化的方式解决这些问题，科学文献中的研究人员创建和描述了算法。科学算法对于理解和重复众多领域的现有工作至关重要。但是，算法通常具有挑战性。同样，由于文档断开连接，相似算法之间的比较也很困难。有关算法的信息主要存在于网站，代码注释等中。没有结构化的元数据来描绘算法。结果，有时会发布多余或类似算法，研究人员从头开始构建它们，而不是重复使用或扩展已经存在的算法。在本文中，我们介绍了一种自动开发知识图（KG）的方法，以解决非结构化数据中的算法问题。由于它更清晰，更广泛地捕获信息，因此算法kg将为算法元数据提供其他上下文和解释性。
There is enormous growth in various fields of research. This development is accompanied by new problems. To solve these problems efficiently and in an optimized manner, algorithms are created and described by researchers in the scientific literature. Scientific algorithms are vital for understanding and reusing existing work in numerous domains. However, algorithms are generally challenging to find. Also, the comparison among similar algorithms is difficult because of the disconnected documentation. Information about algorithms is mostly present in websites, code comments, and so on. There is an absence of structured metadata to portray algorithms. As a result, sometimes redundant or similar algorithms are published, and the researchers build them from scratch instead of reusing or expanding upon the already existing algorithm. In this paper, we introduce an approach for automatically developing a knowledge graph (KG) for algorithmic problems from unstructured data. Because it captures information more clearly and extensively, an algorithm KG will give additional context and explainability to the algorithm metadata.
