Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

This resource is part of our submission to ESWC 2023 resource track, which includes: (i) an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS), (ii) a set of SWeMLS patterns represented based on OPMW and P-Plan ontology, (iii) a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns, (iv) a Knowledge Graph containing machine-actionable metadata from 476 SWeML systems derived during a Systematic Mapping Study (SMS) [1] based on the resource (i-iii), and (v) a Java toolkit for transforming SMS result metadata into a KG. To find the latest SNAPSHOT-version of the resource, we refer interested reader to our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software]