CC BY-NC-ND 4.0 UnportedSay, AysegulFathalla, SaidVahdati, SaharLehmann, JensAuer, SörenAveiro, DavidDietz, JanFilipe, Joaquim2021-03-232021-03-232020https://oa.tib.eu/renate/handle/123456789/6105https://doi.org/10.34657/5153Improvements 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.enghttps://creativecommons.org/licenses/by-nc-nd/4.0/020Semantic WebDomain OntologyOntology EngineeringSemantic PublishingScholarly CommunicationPhysicsBookPartKonferenzschrift