With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously grow in numbers, complexity and components. Online platforms (e.g., OpenML, Kaggle) aim to gather and disseminate ML experiments. However, available knowledge is fragmented with each platform representing distinct components of the ML process or intersecting components but in different ways. To address this problem, we leverage semantic web technologies to model and integrate ML datasets, experiments, software and scientific works into MLSea, a resource consisting of: (i) MLSO, an ontology that models ML datasets, pipelines and implementations; (ii) MLST, taxonomies with collections of ML knowledge formulated as controlled vocabularies; and (iii) MLSea-KG, an RDF graph containing ML datasets, pipelines, implementations and scientific works from diverse sources. MLSea paves the way for improving the search, explainability and reproducibility of ML pipelines. 

MLSea: A Semantic Layer for Discoverable Machine Learning

Creating SHACL shapes for the validation of RDF graphs is a non-trivial endeavor. Automated shape extraction systems typically derive SHACL shapes from RDF graphs, and thus, their effectiveness is inherently influenced by the size and complexity of the RDF graph. However, these systems often overlook the constraints imposed by individual artifacts, although RDF graphs are often constructed by applying ontology terms to heterogeneous data. Only a few systems extract SHACL shapes from either the data schema or the ontology, leading, in either case, to limited or incomplete constraints. We propose SCOOP, a framework that exploits all artifacts associated with the construction of an RDF graph, i.e. data schemas, ontologies, and mapping rules, and integrates the SHACL shapes extracted from each artifact into a unified shapes graph. We applied our approach to real-world use cases and experimental results showed that SCOOP outperforms systems that extract SHACL shapes from RDF graphs, generating more than double the types of constraints than those systems, and effectively identifying missing and erroneous RDF triples during the validation process. Resource type: Software Framework — License: Apache-2.0 DOI: https://doi.org/10.5281/zenodo.10280346 URL: https://github.com/dtai-kg/SCOOP 

SCOOP All the Constraints’ Flavours for Your Knowledge Graph

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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Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

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Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG