## 5 Discussion and Evaluation  
In this paper, an SQWRL-based assessment prototype has been designed and developed for enhancing data integrity regulatory compliance in alignment with ALCOA principles, in the pharmaceutical domain, and based on DIOnt ontology. This prototype utilizes the combined action of generic SWRL rules, with SQWRL queries, on top of DIOnt software architecture, to accurately provide ALCOA assessment. In particular, it checks any batch record of manufacturing plant production, and effectively detects any non-compliant batches regarding ALCOA violations, and, finally, writes down the exact type of failure per each violation. This work has been tested with real world pharma manufacturing data, generated by the pharmaceutical process plant of an industrial partner of SPuMoNI project. An innovative ontological conceptual approach has been chosen due to its inherent semantic representation of heterogeneous pharma production data and processes so as to fully conform with rapidly changing regulatory frameworks and relevant documentation. DIOnt ontology has been specifically designed and developed for ALCOA data integrity regulatory principles. The DIOnt ontology hierarchical structure, with its main ProcessDomain, RegulatoryDomain, and ProductDomain high level classes, along with their multiple lower-level subclasses, as described, allows for a thorough and exhaustive analysis of ALCOA compliance audit mechanisms, described as relations, indicating tasks, roles, and assignments between ontology class individuals. The joint description of these three domain branches can completely cover any pharma regulatory framework, throughout the overall pharma manufacturing production, starting from the batch product composition (ProductDomain), via the appropriate processes required (ProcessDomain), and ending at ALCOA principle compliance via SWRL rule validation with SQWRL queries (RegulatoryDomain). The SQWRL based assessment prototype has been exploited to check ALCOA inconsistency expressed as a 100% violation percentage per principle (Figure 5). Generic SWRL rules have been employed to specify the constraints and restrictions, along with the support of SQWRL-based queries that accurately provide, in numbers, the ALCOA percentage violation, as described in detail in previous sections. In fact, the assessment prototype measures the non-violation success percentage per principle, as seen analytically in JSON printout of Figure 6. The system detects the non-compliant batches and writes down the exact type of failure per each violation, e.g., for the Contemporaneous principle. It then simply calculates the percentage of succeeded, non-violated batches, given a total number of batches. SWRL rule semantic representation capability enhances OWL description logic limitations in cases such as DIOnt, where there are a number of class individuals, each with complex relationships. Specifically, the if-then-else SWRL inherent expression capability offers experienced users the potential of setting their own user defined constraints and subsequently building the new inferred knowledge derived by involved class individuals and their relationships. The updated inferred ontology is developing and is definitely richer in terms of expressivity and completeness. On the other hand, these generic rule-based constraints, by their nature, do not necessarily need to provide memory consuming customized cut and tailored technical details, but generic information and inferred results that could be efficiently reused again and again, thereby improving knowledge usability and reducing memory usage and computation speeds. Then, the SQWRL queries, functioning in a complementary way by extracting information from inferred OWL ontology and its new knowledge base, thus provide ALCOA inconsistency evaluation, as described in the example of Section 3.4. The SQWRL queries would request and refine the precise information level required, which in our case, is the ALCOA violation percentage.