AMV : Algorithm Metadata Vocabulary

TL;DR

This paper introduces the Algorithm Metadata Vocabulary (AMV), a semantic model and OWL-based vocabulary designed to improve the description, searchability, and sharing of algorithm metadata as a knowledge graph.

Contribution

It presents a novel vocabulary for algorithm metadata, developed through a well-defined methodology, enabling better resource description and retrieval.

Findings

AMV can be used to create and publish algorithm metadata as a knowledge graph.

The vocabulary supports metadata services via SPARQL endpoint.

Evaluation indicates promising results in metadata description and retrieval.

Abstract

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.