The Science knowledge graph ontologies, a.k.a. SKGO, is a suite of OWL ontology models to capture the knowledge of scientific research data, via publications, by interlinking domain-specific information, and allow access of these data in a machine-readable, transparent and comparable manner. Currently, SKGO comprises four ontologies for scientific work in various fields of science, including:
- Computer Science (SemSur),
- Physics (PhySci),
- Pharmaceutical science (PharmSci) and
- An upper ontology on top of them called Modern Science Ontology (ModSci).
SKGO ontologies capture the knowledge of scientific information typically presented in publications by interlinking domain-specific information in a highly structured format, thus enabling access to these data in a machine-readable, transparent and comparable manner.
We pay specific attention to a novel ontology for modeling relationships between modern science branches and related entities, such as scientific discoveries, phenomena, renowned scientists, instruments, etc. We followed the Systematic Approach for Building Ontologies (SABiO) when creating the SKGO ontologies.
The main objective is to support the digital transformation of scholarly communication from documents to a knowledge-oriented representation in the form of structured and interlinked knowledge graphs, aiming at analysing, exchanging and exploiting scholarly knowledge in an efficient manner.
The SKGO ontologies can be browsed online, through web-based repository front-end for browsing and visualizing published ontologies, at:
- BioPortal [http://bioportal.bioontology.org/ontologies/MODSCI]
- Linked Open Vocabularies [https://lov.linkeddata.es/dataset/lov/vocabs/modsci]
- and AberOWL [http://aber-owl.net/ontology/ModSci/].
The Wizard for documenting ontologies WIDOCO is used to create HTML documentation for the SKGO ontologies, thus enabling human understandability of the ontologies.
- documentations for SKGO ontologies are available vis their PURL.
- Fathalla, S., Auer, S., & Lange, C. (2020, March). Towards the semantic formalization of science. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 2057-2059).