# AMV : Algorithm Metadata Vocabulary

arXiv: https://arxiv.org/abs/2106.03567 · HTML: https://mlsanity.com/paper/2106.03567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [CODO: An Ontology for Collection and Analysis of Covid-19 Data](https://mlsanity.com/paper/2009.01210/) (2020)

## Built on by (most cited first, 2 of 2)

- [KG-RAG: Bridging the Gap Between Knowledge and Creativity](https://mlsanity.com/paper/2405.12035/) (2024)
- [An Approach for Automatic Construction of an Algorithmic Knowledge Graph from Textual Resources](https://mlsanity.com/paper/2205.06854/) (2022)

## Abstract (arXiv)

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.