Abstract
The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short). However, understanding trends and best practices in this rapidly growing field is hampered by a lack of standardized descriptions of these systems and an annotated corpus of such systems. To address these gaps, we leverage the results of a large-scale systematic mapping study collecting information about 470 SWeMLS papers and formalize these into one resource containing: (i) the SWeMLS ontology, (ii) the SWeMLS pattern library containing machine-actionable descriptions of 45 frequently occurring SWeMLS workflows, and (iii) SWEMLS-KG, a knowledge graph including machine-actionable metadata of the papers in terms of the SWeMLS ontology. This resource provides the first framework for semantically describing and organizing SWeMLS thus making a key impact in (1) understanding the status quo of the field based on the published paper corpus and (2) enticing the uptake of machine-processable system documentation in the SWeMLS area.
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Notes
- 1.
The Neurosymbolic Artificial Intelligence journal will be launched in 2023: https://www.iospress.com/catalog/journals/neurosymbolic-artificial-intelligence.
- 2.
We use Ontotext Refine https://www.ontotext.com/products/ontotext-refine/.
- 3.
Example SHACL-AF rules and SHACL validation constraints can be accessed in our GitHub repo, e.g., https://bit.ly/sweml-t3-pattern for pattern T-3.
- 4.
- 5.
e.g., Garcia et al. [13] https://semantic-systems.net/swemls/System_4QP5XAGX.
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e.g., Garcia et al. [13] in ORKG: https://orkg.org/paper/R574440.
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Source code: https://w3id.org/semsys/sites/swemls-kg/rdf2vec.
- 13.
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- 15.
COoperation DAtabank: https://amsterdamcooperationlab.com/databank/.
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Acknowledgments
This work has been supported by the Austrian Science Fund (FWF) under grant V0745 (HOnEst) and FFG Project OBARIS (Grant Agreement No 877389). SBA Research (SBA-K1) is a COMET Center within the COMET - Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW, and the federal state of Vienna. The COMET Programme is managed by FFG. Moreover, financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, DFG NFDI4DataScience (No. 460234259) and ERC ScienceGRAPH (GA ID: 819536) is gratefully acknowledged.
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Ekaputra, F.J. et al. (2023). Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_22
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