This resource is part of our submission to ESWC 2023 resource track, which includes: Datasets: - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology, - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components, - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS), - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers, - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2]. These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through 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) [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit

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].