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There is enormous growth in various fields of research. This development isaccompanied by new problems. To solve these problems efficiently and in anoptimized manner, algorithms are created and described by researchers in thescientific literature. Scientific algorithms are vital for understanding andreusing existing work in numerous domains. However, algorithms are generallychallenging to find. Also, the comparison among similar algorithms is difficultbecause of the disconnected documentation. Information about algorithms ismostly present in websites, code comments, and so on. There is an absence ofstructured metadata to portray algorithms. As a result, sometimes redundant orsimilar algorithms are published, and the researchers build them from scratchinstead of reusing or expanding upon the already existing algorithm. In thispaper, we introduce an approach for automatically developing a knowledge graph(KG) for algorithmic problems from unstructured data. Because it capturesinformation more clearly and extensively, an algorithm KG will give additionalcontext and explainability to the algorithm metadata.

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