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.

The Circular Economy (CE) domain has a nature of connecting and linking multiple cross-industry domains (e.g., manufacturing and materials) aiming to reduce value loss and avoid waste by building and implementing CE models (i.e., circular value networks) across these domains. In recent years, ontologies have been recognized as a key for representing domain knowledge in CE. Both CE-specific and domain-specific ontologies exist, with more continuously emerging. Matching CE-related ontologies can generate alignments that enhance the interoperability and reusability of such ontologies.In this paper, we present our initial efforts and findings in matching ontologies within the CE domain.

Materials science domain is facing the fourth paradigm of science, i.e., data-driven science, which also encompasses the first three paradigms based on theory, experiment, and simulation. The semiconductor domain is one of many sub-domains of materials science, involving both mathematical models-based simulations and conventional experiments to study materials. A significant challenge in the semicon- ductor domain is the lack of interoperability between materials simulation data and experimental data. While there is existing work, such as the Materials Design Ontology, that enhances the interoperability of simulation data, there remains a need for representing experimental data with rich semantics. To improve the findability, accessibility, interoperability, and reusability of semiconductor experimental data, we present the initial steps in developing a semiconductor domain ontology, SemicONTO.

In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology.In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources.The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.

Circular Economy has the goal to reduce value loss and avoid waste by extending the life span of materials and products, including circulating materials or product parts before they become waste. Circular economy models (e.g., circular value networks) are typically complex and networked, involving different cross-industry domains. In the context of a circular value network, multiple actors, such as suppliers, manufacturers, recyclers, and product end-users, may be involved. In addition, there may be various flows of resources, energy, information and value throughout the network. This means that we face the challenge that the data and information from cross-industry domains in a circular economy model are not built on common ground, and as a result are difficult to understand and use for both humans and machines. Using ontologies to represent domain knowledge can enable actors and stakeholders from different industries in the circular economy to communicate using a common language. The knowledge domains involved include circular economy, sustainability, materials, products, manufacturing, and logistics. The objective of this paper is to investigate the landscape of current ontologies for these domains. This will enable us to in the future explore what existing knowledge can be adapted or used to develop ontologies for circular value networks.