An Approach for Automatic Construction of an Algorithmic Knowledge Graph from Textual Resources
This addresses the issue for researchers who struggle to find and compare algorithms, potentially reducing redundant work, though it appears incremental as it applies existing knowledge graph methods to a new domain.
The paper tackles the problem of inefficient algorithm discovery and comparison due to unstructured documentation by introducing an approach to automatically construct a knowledge graph for algorithms from textual resources, aiming to provide clearer and more extensive metadata.
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.