An Approach for Automatic Construction of an Algorithmic Knowledge Graph from Textual Resources

TL;DR

This paper presents a method to automatically build a knowledge graph of algorithms from unstructured text, enhancing understanding, reuse, and comparison of algorithms across research domains.

Contribution

It introduces a novel approach for extracting and structuring algorithm information into a knowledge graph from textual sources, addressing the lack of structured metadata.

Findings

Creates a comprehensive algorithm knowledge graph from unstructured data

Improves algorithm discoverability and comparison

Enhances explainability of algorithm metadata

Abstract

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.