The Circular Economy (CE) domain is increasingly leveraging ontologies to represent domain knowledge for data sharing and exchange. Several CE-related ontologies have been developed to model CE-specific knowledge for circular value networks. However, CE knowledge representation also relies heavily on existing ontologies from related domains, such as materials and manufacturing, due to the cross-industry nature of CE. Aligning CE-related ontologies is a key step toward enhancing interoperability and reusability. In this paper, we present alignment results and discussions on CE-related ontologies based on an extended ontology survey within the scope of the Onto-DESIDE project.

Performance and age have an intertwined relationship in soccer. However, despite the sport’s popularity and the great value of insight into this topic to decision-making personnel, this is a fairly unexplored topic, particularly for lower-ranked leagues. In this paper, we use a novel performance metric to investigate the peak age among soccer players in Sweden for goalkeepers, defenders, midfielders, and forwards. Each playing position is analyzed separately using univariate and bivariate methods and is also modeled by a hierarchical Bayesian model with player-specific age trajectories.

Units of measure are central in all areas of science and technology. There are several ontologicalframeworks aiming to improve interoperability and precision in digital data exchange of quantitiesinvolving units. We introduce an ontology that specifically targets challenges for handling units acrossdatabases of computational and experimental data from various sources. The ontology is created usingdefinition files from the community-driven OPTIMADE standard for a common API for materialsdatabases. The resulting ontology allows addressing data integration challenges encountered in thateffort, including (i) referencing both specific and more general instances of units that have changedover time; (ii) the use of unit systems to define short domain-relevant identifiers for a collection of unitsthat make sense within a specific subdomain, rather than having to adopt globally standardized namingschemes; (iii) specifications of relationships between units that enables tools to convert between them;and (iv) units not part of the International System of Units (SI) can be represented without defining themin SI units or using SI system conventions. This paper provides a brief survey of existing ontologiesfor units of measure and then presents the design and discuss features of an ontology based on theOPTIMADE unit definitions.

In different sports fields, collecting play-by-play data has become significant for data analysis, as seen in sports like baseball, basketball, ice hockey and football. Badminton remains a relatively new domain in terms of systematically collecting game play data (i.e., play-by-play or shot-by-shot) with only some recently published datasets online. While the game play data provides some detailed information about matches, it lacks rich semantics for complex information retrieval and data analysis. Consequently, the data cannot be used for applications where semantics are needed. This paper introduces a badminton domain ontology - BadmintONTO, along with its illustrative usages to showcase the capability of this ontology to represent basic domain knowledge and to annotate play-by-play data. 

Although analytics is being used in, e.g., the evaluation of players and scouting, it is still challenging to quantify skills and playing styles of players. Such information is important for roster creation and scouting, where teams want to find players that have a playing style that fits within the team, as well as for game preparation to understand the playing style of opponents. In this paper we use player vectors to characterize a player’s playing style. The player vectors contain representations of skills that are computed from game event data. Further, we use fuzzy clustering on the vectors to generate five types of defender playing styles and five types of forward playing styles. For these types, we show the typical skill levels and players with similar styles.

Understanding the role of a particular player, or set of players, in a team is an important tool for players, scouts, and managers, as it can improve training, game adjustments and team construction. In this paper, we propose a probabilistic method for quantifying player roles in ice hockey that allows for a player to belong to different roles with some probability. Using data from the 2021–2022 NHL season, we analyze and group players into clusters. We show the use of the clusters by an examination of the relationship between player role and contract, as well as between role distribution in a team and team success in terms of reaching the playoffs.