Abstract Conceptual models represent the world, but the world changes over time, and is different, to some extent, depending on who you ask. To cater for these aspects, conceptual models must address the representation of temporality and subjectivity. Although some work has been done to incorporate these aspects into conceptual modelling languages, no mainstream language incorporates explicit support for temporality and subjectivity in relation to the existence and predication on the represented entities. In this article, I propose an approach to conceptualising and implementing temporality and subjectivity into conceptual modelling languages as cross-cutting aspects that provide built-in language primitives to the modeller while not imposing any particular manner to represent time or subjects. The approach has been adopted by ConML, a conceptual modelling language geared towards the humanities and social sciences, implemented in the Bundt software tool, and applied to a number of research projects.

Abstract Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse public speeches, news releases, social media posts and other types of discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. Being useful, these advances cannot tackle the nuanced and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a metamodel and modelling approach to represent and analyse discourses. IAT/ML focuses on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three different modelling approaches: ontological, which focuses on what the discourse is about; argumentation, which deals with how the text justifies what it says; and agency, which provides insights into the speakers’ beliefs, desires and intentions. Together, these three modelling approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their understanding, evaluation and fact checking.

Requirements for the analysis, interpretation and reuse of information are becoming more and more ambitious as we generate larger and more complex datasets. This is leading to the development and widespread use of information about information, often called metainformation (or metadata) in most disciplines. The Digital Humanities are not an exception. We often assume that metainformation helps us in documenting information for future reference by recording who has created it, when and how, among other aspects. We also assume that recording metainformation will facilitate the tasks of interpreting information at later stages. However, some works have identified some issues with existing metadata approaches, related to 1) the proliferation of too many “standards” and difficulties to choose between them; 2) the generalized assumption that metadata and data (or metainformation and information) are essentially different, and the subsequent development of separate sets of languages and tools for each (introducing redundant models); and 3) the combination of conceptual and implementation concerns within most approaches, violating basic engineering principles of modularity and separation of concerns. Some of these problems are especially relevant in Digital Humanities. In addition, we argue here that the lack of characterization of the scenarios in which metainformation plays a relevant role in humanistic projects often results in metainformation being recorded and managed without a specific purpose in mind. In turn, this hinders the process of decision making on issues such as what metainformation must be recorded in a specific project, and how it must be conceptualized, stored and managed. This paper presents a review of the most used metadata approaches in Digital Humanities and, taking a conceptual modelling perspective, analyses their major issues as outlined above. It also describes what the most common scenarios for the use of metainformation in Digital Humanities are, presenting a characterization that can assist in the setting of goals for metainformation recording and management in each case. Based on these two aspects, a new approach is proposed for the conceptualization, recording and management of metainformation in the Digital Humanities, using the ConML conceptual modelling language, and adopting the overall view that metainformation is not essentially different to information. The proposal is validated in Digital Humanities scenarios through case studies employing real-world datasets.

The concept of association appears in almost every modelling language, and plays a crucial role in defining how classes (or other kinds of types) can be related to each other, both in conceptual models and code. Often, associations are assumed to be binary (i.e. linking two types), and sometimes higher-arity associations are also considered, such as in UML. However, little attention has been paid to unary associations, which link a type back to itself. Some unary associations establish a symmetric relation on the instances of the type they are attached to, and in this paper we argue that mainstream modelling languages, especially UML, provide no support whatsoever to model this kind of associations, despite being extremely common in real life. To address this need, we propose a simple and powerful metamodel that describes symmetric unary associations, explain how this metamodel has been implemented as part of the ConML conceptual modelling language, and describe how this kind of associations can be implemented in code generation scenarios.

Most literature in conceptual modelling focuses on the development of models. However, models, once created, must be used, which requires that the model is as usable as possible. Often, usage scenarios for a model are only vaguely clear at model creation time, so model usability should be considered as a relevant problem, especially in relation to conformance (creating instance models that conform to a base type model) and extension (creating extended models that build on a base one). In this paper, we propose a particular mechanism, model views, that allow model users to customise, to certain extent, what a model looks like, and thus adapt it to their usage scenario. Model views are fully described, and two case studies regarding very different situations are reported as a form of validation. The results obtained show that model views significantly add flexibility and customisation control during model conformance and extension efforts.

Contemporary software engineering modelling tends to rely on general-purpose languages, such as the Unified Modeling Language. However, such languages are practice-based and seldom underpinned with a solid theory -- be it mathematical, ontological or concomitant with language use. The future of software modelling deserves research to evaluate whether a language base that is compatible with these various elements as well as being philosophically coherent offers practical advantages to software developers.

Most of the information that is used as input for the development of information systems is originally produced in non-structured forms, such as verbal communications or free-style text documents. Some examples are requirements specifications or documents associated to translation of contents. In cases like these, there is a need to support the structuring and extraction of the underlying semantic relations embedded in the text, in order to fully understand and post-process it. There are models for the analysis of textual information, such as information retrieval solutions or topic maps. However, these models do not offer an integral, flexible and reusable approach that can assist in giving structure and semantics to the information within a standard framework. Discourse analysis techniques, in contrast, identify semantic relations (such as authors' intentionality or possible dependencies between text elements) in the textual information according to well-established linguistic patterns. Based on these techniques, this paper presents a modelling language based on the ISO/IEC 24744 metamodel that is capable of representing pieces of textual information in a highly structured manner, describing the semantic relations in the associated discourse. In addition, the paper shows an application of the proposed language to the domain of requirements engineering, illustrating the benefits of the application of the suggested approach as well as its possibilities in other textual domains.

Conceptual modelling allows us to create simplified representations of reality that remove unnecessary detail in order to focus on what is relevant. However, this process of detail removal usually entails an unintended loss of information regarding the temporal variation of reality, the subjective perspectives that there may exist on it, and the different degrees of vagueness that we may have about it. Such “soft” aspects are highly informative about reality, and a conceptual modelling approach capable of capturing them would allow users to create more expressive and accurate models. This paper presents the mechanisms that have been implemented in ConML to cater for the modelling of temporality and subjectivity.

Conceptual modelling is usually accepted as a crucial technique for exploring, understanding, documenting and communicating complex domains. It is then paradoxical that a context where highly complex domains are very common, that of humanities and social sciences specialists reasoning about their fields, lacks appropriate conceptual modelling tools. This paper introduces ConML, a conceptual modelling language designed to be affordable for end users with no previous exposure to information technologies, and created with humanities and social sciences in mind. Some practical experiences about its usage in different scenarios are described, and future research directions and presented.

Business processes are often expressed using a procedural metaphor that emphasizes time-ordering and task sequencing. Although this is relatively easy to model and fits similar mechanistic approaches used in other areas, it is ill-suited for organizations fundamentally composed of humans rather than machines. We argue in this paper that, because of the very nature of humans, a crucial component of businesses, business process should become business methods, and a radically different approach, based on products rather than tasks, should be taken to specify them.

In some areas of software engineering research, there are several metamodels claiming to capture the main issues. Though it is profitable to have variety at the beginning of a research field, after some time, the diversity of metamodels becomes an obstacle, for instance to the sharing of results between research groups. To reach consensus and unification of existing metamodels, metamodel-driven software language engineering can be applied. This paper illustrates an application of software language engineering in the agent-oriented software engineering research domain. Here, we introduce a relatively generic agent-oriented metamodel whose suitability for supporting modeling language development is demonstrated by evaluating it with respect to several existing methodology-specific metamodels. First, the metamodel is constructed by a combination of bottom-up and top-down analysis and best practice. The concepts thus obtained and their relationships are then evaluated by mapping to two agent-oriented metamodels: TAO and Islander. We then refine the metamodel by extending the comparisons with the metamodels implicit or explicit within five more extant agent-oriented approaches: Adelfe, PASSI, Gaia, INGENIAS, and Tropos. The resultant FAML metamodel is a potential candidate for future standardization as an important component for engineering an agent modeling language.

MDA promises a high software return on investment by producing software that evolves easily and is more portable and interoperable. However, MDA has a high price, namely, the need for a unified modelling infrastructure and a rich range of tools to create, maintain and transform models. Without these, model-driven development will not be able to deliver on its promises. This paper presents a common modelling infrastructure plus a collection of tools that, operating on it, can be used to create, edit and interchange models at any abstraction level. By using these tools and the modelling infrastructure, model-driven development is more likely to deliver.