The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants.This paper is an overall presentation of that campaign.

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus).The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign. 

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by18 participants. This paper is an overall presentation of that campaign

Ontologies have been proposed as a means towards making data FAIR (Findable, Accessible, Interoperable, Reusable). This has attracted much interest in several communities and ontologies are being developed. However, to obtain good results when using ontologies in semantically-enabled applications, the ontologies need to be of high quality. One of the quality aspects is that the ontologies should be as complete as possible. In this paper we propose a first version of a tool that supports users in extending ontologies using a phrase-based approach.  To demonstrate the usefulness of our proposed tool, we exemplify the use by extending the Materials Design Ontology.

Ontologies have been proposed as a means towards making data FAIR (Findable, Accessible, Interoperable, Reusable) and has recently attracted much interest in the materials science community. Ontologies for this domain are being developed and one such effort is the Materials Design Ontology. However, to obtain good results when using ontologies in semantically-enabled applications, the ontologies need to be of high quality. One of the quality aspects is that the ontologies should be as complete as possible. In this paper we show preliminary results regarding extending the Materials Design Ontology using a phrase-based topic model.

Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed  a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology.

Additive manufacturing is an innovative production approach aimed at creating products that traditionaltechniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings)or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research andindustry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semantics-aware data management, and enhance the understanding and optimization of additive manufacturingprocesses. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph.

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2018 campaign offered 12 tracks with 23 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation,open evaluation, or consensus). The OAEI2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign.

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.

Circular Economy (CE) aims to reduce value loss and avoid waste by extending the life of products,components, and materials. Circular value networks (CVN), i.e. networks of actors realising partsof the CE, are often complex and involving a multitude of actors, such as suppliers, manufacturers,recyclers, and end-users, from different industry sectors. In addition, the networks enable and managevarious flows of resources, energy, information and value. To set up and operate such networks, datasharing is essential, however, one of the main challenges is semantic interoperability, and as a resultdata are difficult to understand, integrate, and use. Ontologies support semantic interoperability, andcan represent domain knowledge and enable stakeholders to communicate. However, the knowledgedomains involved are many, including sustainability, materials, products, manufacturing, and logistics,where well-established ontologies already exist. In addition, these domains need to be connected torelevant industry sectors. In order to bridge these domains we propose a set of core ontology modules,allowing to express links between existing ontologies as well as filling gaps related to core CE concepts.

Background: One of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005. In 2005 and 2006 the task in this track was to align the Foundational Model of Anatomy and the OpenGalen Anatomy Model. Since 2007 the ontologies used in the track are the Adult Mouse Anatomy and a part of the NCI Thesaurus. Since 2015 the data in the Anatomy track is also used in the Interactive track of the Ontology Alignment Evaluation Initiative.

Results: In this paper we focus on the Anatomy track in the years 2007-2016 and the Anatomy part of the Interactive track in 2015-2016. We describe the data set and the changes it went through during the years. Further, we give an overview of all systems that participated in the track and the techniques they have used. We discuss the performance results of the systems and summarize the general trends.

Conclusions: About 50 systems have participated in the Anatomy track. Many different techniques were used. The most popular matching techniques are string-based strategies and structure-based techniques. Many systems also use auxiliary information. The quality of the alignment has increased for the best performing systems since the beginning of the track and more and more systems check the coherence of the proposed alignment and implement a repair strategy.Further, interacting with an oracle is beneficial.

As we live in a world of limited resources, the transition from a linear economic model to a circular model is crucial. The Circular Economy (CE) paradigm aims to maintain material continuity through the cycle of production, consumption and recycling. The Digital Product Passport (DPP) is currently recognised as a critical instrument for advancing CE, serving as a comprehensive digital repository for product lifecycle information. The DPP paradigm fosters transparency and traceability. However, so far there is no agreed-upon standard for technically representing and expressing DPPs. This paper aims to provide a comprehensive analysis of the requirements of a general (cross-sectoral) DPP, and to discuss the representation of a core DPP model. We propose to express this in the form of an ontology network, i.e., a formal model serving as a “translation layer” from raw data to interpreted information, along with SHACL shapes for increased data quality and validation. Despite existing research on DPPs, a comprehensive tool enabling this transition into using DPPs is yet to be developed, making this paper a pioneering exploration into the modelling of a DPP core ontology.

This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI.

Ice hockey is a highly popular sport that has seen significant increase in the use of sport analytics. To aid in such analytics, most major leagues collect and share increasing amounts of play-by-play data and other statistics. Additionally, some websites specialize in making such data available to the public in user-friendly forms. However, these sites fail to capture the semantic information of the data, and cannot be used to support more complex data requirements. In this paper, we present the design and development of an ice hockey ontology that provides improved knowledge representation, enables intelligent search and information acquisition, and helps when using information from multiple databases. Our ontology is substantially larger than previous ice hockey ontologies (that cover only a small part of the domain) and provides a formal and explicit representation of the ice hockey domain, supports information retrieval, data reuse, and complex performance metrics.

To speed up the progress in the field of materials design, a number of challenges related to big data need to be addressed. This entry discusses these challenges and shows the semantic technologies that alleviate the problems related to variety, variability, and veracity.

To speed up the progress in the field of materials design, a number of challenges related to big data need to be addressed. This entry discusses these challenges and shows the semantic technologies that alleviate the problems related to Variety, Variability, Veracity and FAIRness.

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.

The Semantic Web aims to make data on the web machine-readable by introducing semantics to the data. Ontologies are one of the critical technologies in the Semantic Web. Ontologies, which provide a formal definition of a domain of interest, can play an important role in enabling semantics-aware data access and data integration over heterogeneous data sources. Traditionally, ontology-based data access and integration methods focus on data that follows relational data models. However, in some domains, such as materials design, the models that data follows and the methods by which it is shared differ today. Data may be based on different data models (i.e., relational models and non-relational models) and may be shared in different ways (e.g., as tabular data via SQL queries or API (Application Programming Interface) requests, or as JSON-formatted data via API requests). To address these challenges, conventional ontology-based data access and integration approaches must be adapted. The recently developed GraphQL, a framework for building APIs, is an interesting candidate for providing such an approach, although the use of GraphQL for integration has not yet been studied.

In this thesis, we propose a GraphQL-based framework for data access and integration. As part of this framework, we propose and implement a novel approach that enables automatic generation of GraphQL servers based on ontologies rather than building them from scratch. The framework is evaluated via experiments based on a synthetic benchmark dataset. Further, we utilize the field of materials design as a target domain to evaluate the feasibility of our framework by showing the use of the framework for the Open Databases Integration for Materials Design (OPTIMADE), which is a community effort aiming to develop a specification for a common API to make materials databases interoperable. At the beginning of this work, no ontologies existed for the domain of computational materials databases. As our approach requires the use of an ontology, we developed one: the Materials Design Ontology (MDO). Furthermore, when new databases are added or new kinds of data are added to existing databases, the coverage of the ontology driving the GraphQL server generation may need to be enlarged. Therefore, we study how ontologies can be extended and propose an approach based on phrase-based topic modeling, formal topical concept analysis and domain expert validation. In addition to extending MDO, we also use this approach to extend two ontologies in the nanotechnology domain.

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.

In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontology-based techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.

In the data-driven workflows in the materials science domain, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontology-based techniques are an important way to reduce these problems and a number of efforts have started. In this paper, we use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert.

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.

User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In this paper, we present a broad study on user validation of ontology alignments that encompasses three distinct but inter-related aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects and provide an overview of how current systems address them. Finally, we use experiments from the Interactive Matching track of the Ontology Alignment Evaluation Initiative 2015–2018 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.

A GraphQL server contains two building blocks: (1) a GraphQL schema defining the types of data objects that can be requested; (2) resolver functions fetching the relevant data from underlying data sources. GraphQL can be used for data integration if the GraphQL schema provides an integrated view of data from multiple data sources, and the resolver functions are implemented accordingly.However, there does not exist a semantics-aware approach to use GraphQL for data integration.We proposed a framework using GraphQL for data integration in which a global domain ontology informs the generation of a GraphQL server. Furthermore, we implemented a prototype of this framework, OBG-gen. In this paper, we demonstrate OBG-gen in a real-world data integration scenario in the materials design domain and in  a synthetic benchmark scenario.

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 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. 

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.

The Simple Standard for Ontological Mappings (SSSOM) was first published in December 2021 (v. 0.9). After a number of revisions prompted by community feedback, we have published version 0.15.0 in July 2023. Here we report on the progress made since August 2022, in particular changes to tooling, data model and summary of ongoing standardisation efforts.