This web page provides a list of resources related to our ESWC 2023 submission entitled “Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG”. The resources includes:
- The SWeMLS ontology documentation: [Link]
- The Zenodo URL for a static snapshot of the SWeMLS-artefacts [Link], which contains:
- The SWeMLS ontology,
- The SWeMLS pattern library,
- The SWeMLS SHACL constraints validation,
- The SWeMLS SHACL-AF rules for KG enrichment, and
- The SWeMLS-KG dump comprising all four elements above.
- The SWeMLS toolkit, a simple java program supporting the creation process of SWeMLS-KG [Link]
- The SWeMLS-KG linked data interface
- The SWeMLS ORKG observatory [Link]
- A Jupyter notebook for running rdf2vec on SWeMLS-KG [Link]
SWeMLS-KG Creation Process
The resources listed are parts of the KG creation process as depicted in the Figure below. We will briefly explain the main steps (denoted with numbers and alphabets in the figure) in the following:
- Systematic Mapping Study. The starting point for the process was our prior large-scale SMS on the topic of SWeMLS  (cf. Step 1 in in the Figure) during which we collected information from 476 papers on SWeMLS in spreadsheet format. Starting from this SMS, we converted its results in a machine-processable format, through the next steps
- SWeMLS Conceptualization. We conceptualised the SWeMLS related information to produce three types of outputs: (a) the SWeMLS ontology (b) the SWeMLS pattern library, which consists of pattern templates represented as RDF instances and SHACL-Advanced Features (SHACL-AF) rules, and © SWeMLS constraint definitions, which provide users with a mean to validate SWeMLS instances based on the existing patterns.
- KG Population, Enrichment, and Validation. We perform the population of SWeMLS-KG with the SWeMLS data and the artifacts produced in Step 2 supported by the SWeMLS-toolkit. The integrated and validated SWeMLS-KG is published through three distribution channels: (i) Linked Data interface, (ii) ORKG observatory, and (iii) a Zenodo repository.
 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 (accepted for publication). ACM CSUR (2023)