## 1 Introduction  
The pharma industry operates in a condition of unlimited need for the constant innovation and speedy delivery of efficient, safe, and quality pharmaceutical products. Hence, it comes as no surprise that the pharmaceutical sector is heavily regulated. The pharma industry is under the constant and vigorous scrutiny of organizations that enforce regulatory compliance, such as the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). These organizations establish regulations for pharmaceutical industries, with the overall aim to protect public health. In pharma manufacturing, regulatory compliance refers to the capability of a pharma manufacturing plant's management system to define and comply with a set of regulations, emanating from pharma production processes. Unfortunately, inappropriate practices that lead to regulatory violations are not rare in the pharma industry. The number of notices of concern, statements of non-compliance, and warning letters issued by the World Health Organization (WHO), EMA, and FDA have shown an alarming increase in recent years [1]. According to the WHO, in the pharmaceutical industry context, data integrity is _\"the degree to which data are complete, consistent, accurate, trustworthy and reliable and these characteristics of the data are maintained throughout the data life cycle. The data should be collected and maintained in a secure manner, such that they are attributable, legible, contemporaneously recorded, original or a true copy and accurate.\"_[2]. Therefore, when actions take place, either deliberately or accidentally, which result in data not bearing the aforementioned characteristics,data integrity is considered to be compromised. In 2003, the FDA assembled the compliance requirements for data integrity in 21 CFR Part 11 by devising the acronym ALCOA, which means that data must be Attributable, Legible, Contemporaneous, Original, and Accurate. ALCOA was later extended to ALCOA+ by adding the Complete, Consistent, Enduring, and Available principles. It is common in the relevant literature to use the acronym ALCOA to include all the principles from both ALCOA's initial version and ALCOA+, and this approach is also followed in the current study. Apart from the FDA, ALCOA has also been endorsed by the European Commission in cGMP Annex 11. There have been many cases relevant to problematic regulatory compliance of the pharma industry. Indicatively, 21 out of 28 warning letters issued by the FDA to pharmaceutical companies between January 2015 and May 2016 were relevant to data integrity violations [1]. Another report indicates that the increase of FDA warning letters pertinent to data integrity has been exponential and that the corresponding number quintupled from 2014 to 2017 [3]. The work presented in this study is part of the SPuMoNI R&D project, which is funded by the European Union's Horizon Europe Framework Programme for Research and Innovation. The SPuMoNI project has been developed for providing data authenticity, data quality, compliance, and audibility within a pharma manufacturing environment [4], via adoption of ontologies, commonly utilized for pharma conceptual modeling [5]. The SPuMoNI technology is offering means to build an explicit ontology model of the pharma production processes domain, which is data storage independent, is both machine and human readable, and supports knowledge management as well as inferential reasoning. The aim of this study is the provision of a proof-of-concept prototype, based on ontology conceptual design, which takes advantage of a combined utilization of generic semantic web rule language (SWRL) rules with semantic query-enhanced web rule language (SQWRL) queries for performing ALCOA assessments, and thus to support the corresponding required regulatory compliance. This paper is structured as follows.