AMV : Algorithm Metadata Vocabulary
This addresses the issue for researchers and practitioners who struggle to find relevant algorithms, though it is incremental as it builds on existing metadata vocabulary concepts.
The authors tackled the problem of poor search results for algorithms by developing the Algorithm Metadata Vocabulary (AMV), a semantic model represented as an OWL file that enables creation of knowledge graphs and SPARQL endpoints, with evaluation showing promising results.
Metadata vocabularies are used in various domains of study. It provides an in-depth description of the resources. In this work, we develop Algorithm Metadata Vocabulary (AMV), a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). The snag faced by the researchers in the current time is the failure of getting relevant results when searching for algorithms in any search engine. AMV is represented as a semantic model and produced OWL file, which can be directly used by anyone interested to create and publish algorithm metadata as a knowledge graph, or to provide metadata service through SPARQL endpoint. To design the vocabulary, we propose a well-defined methodology, which considers real issues faced by the algorithm users and the practitioners. The evaluation shows a promising result.