The vast and growing web of rules and regulations that govern modern organisations is increasingly difficult to comprehend and manage. One technique for understanding and managing rules among stakeholders is enterprise modelling of organisational rules. However, rule models are usually flat, lacking hierarchical structure that allows for effective abstraction and reduced complexity. The aim of this paper is to examine the different aspects of hierarchy among organisational rules. A model pattern is proposed, based on the main constructs rule description, norm hierarchy, scope, and citation. The model pattern is demonstrated by instantiating it in a concrete case, the European Union’s General Data Protection Regulation (GDPR), using Wikibase Cloud to structure the data and Kumu.io to visualise it.

Legal regulations governing digital infrastructures and data spaces, such as the European Health Data Space (EHDS), are becoming increasingly complex and difficult to interpret, particularly for non-experts in law, including citizens and small and medium-sized enterprises. To address this challenge, we propose a method for visualising legal regulations that integrates legal ontology with legal design techniques. The method proceeds through three main stages: an ontology-informed reading of regulatory text supported by large language models (LLMs); the pre-processing of ontological constructs into structured tables enriched with explanations and examples; and the rendering of interactive concept maps using visualisation tools such as Kumu. In order to show the feasibility of the approach, we demonstrate the method using the EHDS as a case. The approach not only supports legal understanding but also contributes to the broader goal of trustworthy digital governance.

Rules and regulations grow into complex webs that are difficult even for experts to overview and comprehend. Particularly in cross-organisational collaborations in heavily regulated practices, e.g. health information exchange, rules and actors may form multilevel, adaptive, organisational rule systems. One potential way to analyse these complex structures is by utilising enterprise modelling and visualisations. This paper proposes a conceptual model for the description of rules in multilevel adaptive collaborations. The model is demonstrated with a case of collaboration for health data exchange, representing how actors at different levels adapt their rules according to their goals and how these goals must be balanced against the goals of the collaboration.

Human rights groups of all sizes and specializations gather evidence of human rights violations. Those with sufficient resources commonly use dedicated databases to manage the intricate web of sources, events, and analyses that build a strong case. However, developing databases for the needs of each organization remains a challenge. Effective database design requires a data model, which functions as a blueprint for how the data is structured and related. A most basic example of what a data model can specify is that a human rights violation is committed by at least one perpetrator, and that the perpetrator’s attributes, such as name or date of birth, should be recorded. A data model that is shared and generic reduces the need to reinvent the wheel since its design can be reused for several databases. In the information systems field, such shared models are called ontologies. Despite the critical nature of the matter, no ontology for human rights violations documentation exists. The present note, therefore, will present the design of the first ontology of this kind. It was developed in association with the human rights group HURIDOCS, which specializes in information management. The requirements elicitation included unstructured interviews with HURIDOCS, document analysis of human rights manuals, and a survey with practitioners. The resulting ontology, named OntoRights, is freely available online with an open license. The evaluation of OntoRights suggests that OntoRights could be highly useful for case databases.

Health care is a complex domain containing large amounts of data, including clinical and administrative data. Furthermore, the domain includes advanced decision-making utilising the collected data. Various IT systems based on AI technologies, such as machine learning, have been promoted as a way to improve both the quality and efficiency of health care. So far, the focus has been on supporting quite narrow and data-intensive activities carried out by a single actor, such as interpreting X-ray images and performing triage. However, providing health care for a single patient can involve a comprehensive process with numerous actors, ranging from home care and primary care to specialist care. In this paper, we examine how existing AI solutions can support a complex care process involving several collaborating actors. We base the examination on a health care case from Swedish elderly care. The case is used to identify multiple problem areas, which are then compared to existing AI solutions

Organisations are increasingly governed by complex and multilayered systems of rules and regulations. In heavily regulated sectors such as healthcare, compliance with these rules poses significant challenges. Not only can rule compliance conflict with organisational goals, but varying interpretations by enforcement actors can also lead to conflicting or unpredictable outcomes. Decisions by regulatory authorities and courts, equipped with fines and other sanctioning powers, create new institutional realities for organisations. One way to examine and understand these complex systems of rules and actors is through the use of ontologies. This paper aims to investigate how the UFO ontology, and, specifically, the legal core ontology UFO-L, can be used, or extended, for creating patterns for modelling of organisational rule enforcement. To facilitate this investigation, a legal case regarding Swedish healthcare was selected to identify requirements for an ontology for legal enforcement. These requirements were then compared with the representation capabilities of UFO-L. The investigation revealed strengths and weaknesses of UFO-L and provided suggestions for its modification.

Organisational rules, both created internally and externally mandated, are vital to an enterprise. Yet, understanding and managing these rules is problematic, as they are a part of a complex system. Thus, there is a need to view them in a complex setting of organisational actors and interactions. It has been suggested that enterprises, particularly in situations like collaboration in healthcare, should be analysed as complex adaptive systems (CAS). However, only some enterprise modelling contributions can represent perspectives of CAS theory. In this paper, we set out to examine how organisational rules in complex adaptive systems has been modelled. A systematic mapping study was conducted on modelling languages of organisational rules in collaborations, resulting in 22 identified languages. The constructs and modelling patterns of the identified languages were mapped against an analytical framework that included 15 concepts from CAS theory. Overall, even though most CAS concepts had yet to be addressed by the identified languages, potentially useful approaches were found, related to: abstraction of large organisational rule systems through power relations; interpretation and implementation of rules; feedback loops to rule-makers, including delays.

Organisational rules and regulations are vital components of a business. However, their growing numbers, interdependencies, and often unpredictable interactions with social agents make them challenging to manage. Enterprise modelling has proven an effective technique for sensemaking and creating a shared understanding of organisational structures, such as rules and goals. However, what is captured or not in a model depends on the theory used to examine the organisation, whether implicit or explicit. Particularly in healthcare, many scholars view organisations from the lens of Complex Adaptive Systems (CAS), rather than General Systems Theory (GST). This paper discusses how enterprise modelling of organisational rules grounded in CAS theory will have a different focus than if grounded in GST. Four key themes for are identified: abstraction, rule-agent interaction, emergence, and feedback channels. Each is discussed in light of privacy regulation and healthcare practice, and proposals are made for future research directions in enterprise modelling.

Ontology-driven conceptual modeling, Human rights, Requirements elicitations, Information infrastructures, Unified Foundational Ontology (UFO), OntoUML, OntoRights

Human rights groups of varying sizes collect information about human rights violations. Managing this information in a structured way is a challenging task, particularly since many human rights groups have limited budgets. This paper presents the initial work with requirements elicitation leading to the design of OntoRights - a domain ontology for human rights violations documentation that aims at helping human rights organisations to structure their documentation. To ensure that the ontology was grounded in practice, two interconnected surveys of manuals and practitioners were conducted. Moreover, the design used information infrastructure theory to increase the ontology’s potential uptake. The resulting ontology extends the legal core ontology UFO-L and is represented in OntoUML. As a demonstration, it was instantiated twice: in a fictitious and in a real case.