A Modular Ontology for MODS -- Metadata Object Description Schema

Rushrukh Rayan, Cogan Shimizu, Heidi Sieverding, Pascal Hitzler
7/31/2023
cs.CLcs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

NFDIcore 2.0: A BFO-Compliant Ontology for Multi-Domain Research Infrastructures

82% match2024

Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance

81% match2024

NFDI4DSO: Towards a BFO Compliant Ontology for Data Science

80% match1 repo42024

DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering

80% match2023

Systematic Analysis of COVID-19 Ontologies

80% match2023

MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization

79% match2023

Cite this paper

@article{rayan2023modular,
  title  = {A Modular Ontology for MODS -- Metadata Object Description Schema},
  author = {Rushrukh Rayan and Cogan Shimizu and Heidi Sieverding and Pascal Hitzler},
  year   = {2023},
  eprint = {2308.00116},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.00116v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.00116.svg)](https://codeofpaper.com/paper/2308.00116)

Discussion