There is a growing producer and consumer interest in medical devices and the commensurate need for regulatory frameworks to ensure the quality of medical devices marketed locally and globally. This work focuses on formalizing the clauses enacted by Regulation (EU) 2017/745 for risk-based classification and class-based conformity assessment regarding marketability of medical devices. The resulting knowledge base (KB) represents clauses in Positional-Slotted Object-Applicative (PSOA) RuleML by integrating F-logic-like frames with Prolog-like relationships for atoms used as facts and in the conclusions and conditions of rules. Rules can apply polyadic functions, define polyadic relations, and augment conclusions with actions and conditions with events. The PSOA RuleML-implemented Medical Devices Rules KB was tested by querying in the open-source Java-implemented PSOATransRun system, which has provided a feedback loop for refinement and extension. This prototype can contribute to the licensing process of stakeholders and the registration of medical devices with a CE conformity mark.

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Wearable robots are devices intended to improve the quality of users’ life by augmenting, assisting, or substituting human functions. Exoskeletons are one of the most widespread types of wearable robots, currently used extensively in medical applications (and also for industrial, assistive, or military purposes), thus governed by regulations for medical devices and their conformity assessment. On top of that, manufacturers must also specify if their exoskeletons can be categorized as machines and, therefore, additionally apply a number of requirements mandated from machinery regulations. This work focuses on capturing both the abovementioned requirements enacted by the Medical Devices Directive 2017/745 and the Machinery Directive 2006/42 into a single framework. It formalizes into Rules the Conformity Assessment procedures regarding the marketability of exoskeletons indicated by the CE marking (“Conformité Européene”). These Rules, expressed in the Positional-Slotted Object-Applicative (PSOA) RuleML code, were complemented by representative Facts based on real-life cases of commercialized exoskeletons. Additional Exoskeletons Facts can be included by users from other forms (such as MS Excel) and translated into the PSOA RuleML code through the provided Python script. The open-source Exoskeletons’ CE mark (ExosCE) Rules KB was tested by querying in the open-source PSOATransRun system. The ExosCE Rules prototype can assist in the compliance process of stakeholders and in the registration of exoskeletons with a CE mark.

This paper studies Knowledge Bases (KBs) in PSOA RuleML and IDP, aligning, interoperating, and co-executing them for a use case of Air Traffic Control (ATC) regulations. We focus on the common core of facts and rules in both languages, explaining basic language features. The used knowledge sources are regulations that are specified in (legal) English, and an aircraft data schema. In the modeling process, inconsistencies in both sources were discovered. We present the discovery process utilizing both specification languages, and highlight their unique features. We introduce three extensions to this ATC KB core: 1) While the current PSOA RuleML does not distinguish the ontology separately from the instance level, IDP does. Hence we specify a vocabulary-enriched version of ATC KB in IDP for knowledge validation. 2) While the current IDP uses relational modeling, PSOA also supports graph modeling. Hence we specify a relationally interoperable graph version of ATC KB in PSOA.3) The KB is extended to include optimization criteria. With this, the determination of an optimal sequence of more than two aircraft is possible.