Building on studies of historical non-Westphalian world orders, this article challenges desires to identify a ‘first’ international. The question ‘when was the first international?’ is fundamental to defining disciplinary boundaries and the ontologies that shape them. Such quests are substantialist rather than relational, limiting our understanding of the relations and diversity of agents involved in world ordering. Existing approaches to the ‘first’ international are caught in a ‘Mesopotamian trap’: a combination of social evolution conceptual models grounded in colonial epistemologies, analytical presentism, and the surviving propaganda of ancient urban rulers. This article proposes ‘dynamic multiplicity’ as a new framework to account for the diversity, complexity, dynamism, and relationality of world orders in past and present. Dynamic multiplicity emphasizes: a quantitative and qualitative multiplicity of actors; never-ending and always unfolding relations; the instability and permeability of social actors; the diachronic nature of social action; hierarchical and heterarchical power relations; the multi-scalar spatiality of the social; and sustained and critical interdisciplinarity. It applies dynamic multiplicity to the case of Sumer and ancient West Asia, a so-called ‘first’ international, to reveal a diversity of durable relational actors and contradict assumptions that international relations necessarily lead to world orders of homogenous unit-types.

The amount of data within the product development process requires a structured approach to coordinate them. Knowledge management solutions, such as ontologies, are a suitable way of linking data and representing semantic relationships. However, making all relevant data usable to ensure their target-oriented application is still a challenge. Thus, this contribution presents an approach to identify and classify heterogeneous data in product development. Besides this single ontology approach, interface solutions for data integration into an ontology are proposed.

Engineering standards are an important source of knowledge in product development. Despite the increasing digitalisation, the provision and usage of standards is characterised by lots of manual steps. This research paper aims at applying automatic knowledge graph creation in the domain of engineering standards to enable machine-actionable standards. For this, a formula knowledge graph ontology as well as suitable information extraction techniques are developed. The concept is validated using the example of DIN ISO 281, showing the overall capability of automatic knowledge graph creation.

In research environments and laboratories e.g. for material sciences the in- and output of simulation data is manually managed. Therefore, physical experiments as well as simulations might be carried out several times, learnings are not systematically gathered, and experiments do not systematically build on learnings from data. This paper proposes to engage an ontology in conjunction with a simulation to use data from already carried out experiments and on that basis predict material behaviour under certain condition and plan further physical experiments.

This paper presents a mapping of theory use in the design discipline based on the corpus of the published ICED and DESIGN conference papers since 2010. We searched the resulting 4,451 papers for occurrences of theories and compared them with an existing ontology of named theories through natural language processing (NLP). The results yielded a variety of analyses, illustrating, for example, the most-used theories and which disciplines these theories stem from. This paper presents a rich overview of the theories relevant to the design discipline and a novel approach to bibliometric analyses.

As design evolves, language serves as a bridge between envisioned futures and the ontological elements of design that shape them. This manuscript presents an alternative glossary that gathers words from diverse disciplines and practices intersected by a decolonial lens that challenges hegemonical narratives. The glossary of the world to come results from a three-day workshop that focused on language as a formal, normative, and subversive tool capable of defining future behaviour and destabilizing the present. The terms are some among the many that exist to form this decolonial world.

A core predicate of Industry 5.0 (I5.0) is the integration of human, environmental and social factors with new technologies. The integration of collaborative robots offers increased productivity but raises questions on safety and how robots can respond to varying cognitive and physical attributes. This paper discusses the significance of structured ontologies in managing complex information for proactive, safe, and productive human-robot collaboration. The paper highlights the future work to be undertaken to ensure the safe and fluid integration of humans and robots within I5.0.

The widespread use of finite-element analysis (FEA) in industry has led to a large accumulation of cases. Leveraging past FEA cases can improve accuracy and efficiency in analyzing new complex tasks. However, current engineering case retrieval methods struggle to measure semantic similarity between FEA cases. Therefore, this article proposed a method for measuring the similarity of FEA cases based on ontology semantic trees. FEA tasks are used as indexes for FEA cases, and an FEA case ontology is constructed. By using named entity recognition technology, pivotal entities are extracted from FEA tasks, enabling the instantiation of the FEA case ontology and the creation of a structured representation for FEA cases. Then, a multitree algorithm is used to calculate the semantic similarity of FEA cases. Finally, the correctness of this method was confirmed through an FEA case retrieval experiment on a pressure vessel. The experimental results clearly showed that the approach outlined in this article aligns more closely with expert ratings, providing strong validation for its effectiveness.

This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.

Metamodeling is a general approach to expressing knowledge about classes and properties in an ontology. It is a desirable modeling feature in multiple applications that simplifies the extension and reuse of ontologies. Nevertheless, allowing metamodeling without restrictions is problematic for several reasons, mainly due to undecidability issues. Practical languages, therefore, forbid classes to occur as instances of other classes or treat such occurrences as semantically different objects. Specifically, meta-querying in SPARQL under the Direct Semantic Entailment Regime uses the latter approach, thereby effectively not supporting meta-queries. However, several extensions enabling different metamodeling features have been proposed over the last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2 QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in Lenzerini et al. (2015, Description Logics) and Lenzerini et al. (2020, Information Systems 88, 101294), Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). A reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et al. (2017, Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, 1–6). In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. These tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge Bases). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies with limited resources (time and memory). This paper significantly extends Qureshi and Faber (2021, International Joint Conference on Rules and Reasoning, Springer, 218–233.) by a more detailed experimental analysis and more background.