Deriving Semantic Validation Rules from Industrial Standards: an OPC UA Study

Yashoda Saisree Bareedu, Thomas Frühwirth, Christoph Niedermeier, Marta Sabou, Gernot Steindl , Aparna Saisree Thuluva, Stefani Tsaneva, Nilay Tufek Ozkaya

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung


Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the prescribed guidelines. This increases the risk of costly interoperability errors induced by the incorrect use of the standards. There is therefore an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. We exemplify an adaptation of this approach in the context of the OPC UA industrial standard and conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify constraints in the specification documents by inspecting the tables (P=87%) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.
FachzeitschriftSemantic Web
PublikationsstatusVeröffentlicht - 2023
  • ORE2

    Sabou, M. & Disselbacher-Kollmann, K.


    Projekt: Auftragsforschung