Semantic Representation of Physics Research Data

Aysegul Say, Said Fathalla, Said Fathalla, Sahar Vahdati, Sahar Vahdati, Jens Lehmann, Jens Lehmann, Sören Auer, Sören Auer

2020

Abstract

Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.

Paper Citation

in Harvard Style

Say A., Fathalla S., Vahdati S., Lehmann J. and Auer S. (2020). Semantic Representation of Physics Research Data. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD; ISBN 978-989-758-474-9, SciTePress, pages 64-75. DOI: 10.5220/0010111000640075

in Bibtex Style

@conference{keod20,
author={Aysegul Say and Said Fathalla and Sahar Vahdati and Jens Lehmann and Sören Auer},
title={Semantic Representation of Physics Research Data},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD},
year={2020},
pages={64-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010111000640075},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD
TI - Semantic Representation of Physics Research Data
SN - 978-989-758-474-9
AU - Say A.
AU - Fathalla S.
AU - Vahdati S.
AU - Lehmann J.
AU - Auer S.
PY - 2020
SP - 64
EP - 75
DO - 10.5220/0010111000640075
PB - SciTePress