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In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.

In this work, we outline an approach for question answering over regulatory documents. In contrast to traditional means to access information in the domain, the proposed system attempts to deliver an accurate and precise answer to user queries. This is accomplished by a two-step approach which first selects relevant paragraphs given a question; and then compares the selected paragraph with user query to predict a span in the paragraph as the answer. We employ neural network based solutions for each step, and compare them with existing, and alternate baselines. We perform our evaluations with a gold-standard benchmark comprising over 600 questions on the MaRisk regulatory document. In our experiments, we observe that our proposed system outperforms other baselines.

Industry 4.0 is a global endeavor of automation and data exchange to create smart factories maximizing production capabilities and allowing for new business models. The Reference Architecture Model for Industry 4.0 (RAMI 4.0) describes the core aspects of Industry 4.0 and defines Administration Shells as digital representations of Industry 4.0 components. In this paper, we present an approach to model and implement Industry 4.0 components with the Resource Description Framework (RDF). The approach addresses the challenges of interoperable communication and machine comprehension in Industry 4.0 settings using semantic technologies. We show how related standards and vocabularies, such as IEC 62264, eCl@ss, and the Ontology of Units of Measure (OM), can be utilized along with the RDF-based representation of the RAMI 4.0 concepts. Finally, we demonstrate the applicability and benefits of the approach using an example from a real-world use case.