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.

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.

Success in the life sciences depends on access to information in knowlegde bases and literature. Finding and extracting the relevant information depends on a user’s domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant queries in response to keyword input from the user. Queries are presented in natural language, translated to formal query syntax and issued to a knowledge base of scientific literature, documents or aligned document segments. We describe the components of the system and exemplify using real-world examples.

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.

Circular economy aims at reducing value loss and avoiding waste, by circulating material or productparts before they become waste. Today, lack of support for sharing data in a secure, quality assured, andautomated way is one of the main obstacles that industry actors point to when attempting to create newcircular value networks. Together with using different terminologies and not having explicit definitions ofthe concepts that appear in data, this makes it very difficult to create new ecosystems of actors in Europetoday. A solution to these challenges needs to leverage open standards for semantic data interoperabilityin establishing a shared vocabulary (ontology network) for data documentation, as well as create adecentralized digital platform that enables collaboration in a secure and confidentiality-preservingmanner. This vocabulary can then be used to construct digital twins of circular value networks to furtherenable open collaboration. Once defined, the blueprints of these digital twins will be reusable as templatesand can be reused with a different set of actors, or used within a different industry domain. This visionincludes a number of open research problems, including the development of ontologies that need to modela wide range of different materials and products, not only providing vertical interoperability but alsohorizontal interoperability, for cross-industry value networks. As well as transdisciplinary research onmethods to find, analyse and assess new circular value chain configurations, and form their decentralizeddigital twins. The solutions will allow for automation of planning, management, and execution of circularvalue networks, at a European scale, and beyond. Thereby supporting the acceleration of the digitaland green transitions, automating the discovery and formation of new collaborations in the circulareconomy.

LINHAC 2023 took place June 7-9, 2023, and was organized by Linköping University (Patrick Lambrix and Niklas Carlsson) and Linköping Hockey Club (Mikael Vernblom). LINHAC brought together professionals and academics with an interest in hockey analytics. It featured the latest research in hockey analytics in academia and companies, discussions with analysts and coaches, industry sessions with the latest hockey analytics products, and an analytics competition for students.

LINHAC 2024 took place June 3-5, 2024, and was organized by Linköping University and Linköping Hockey Club. LINHAC brought together professionals and academics with an interest in hockey analytics. It featured the latest research in hockey analytics in academia and companies, discussions with analysts and coaches, industry sessions with the latest hockey analytics products, and an analytics competition for students.

Ontologies are an important technology for the Semantic Web and many ontologies have already been developed. Many ontologies also contain overlapping information and to be able to use them together effectively, we need to align them. Some of the current alignment techniques use information about the structure of the ontologies, but they have not produced good results in evaluations. We propose an approach where, in contrast to the other approaches, structural information is used as a filtering method in the alignment process. We evaluate the approach in terms of quality and performance.

One of the current challenges in ontology alignment is scalability and one technique to deal with this issue is to reduce the search space for the generation of mapping suggestions. In this paper we develop a method to prune that search space by using clustering techniques and topic identification. Further, we provide experiments showing that we are able to generate partitions that allow for high quality alignments with a highly reduced effort for computation and validation of mapping suggestions for the parts of the ontologies in the partition. Other techniques will still be needed for finding mappings that are not in the partition.

Traffic accidents account for more than half a million casualties every year. The analysis of accidents helps identifying the elements that affect traffic conditions, the relationships between them, and how they may contribute to the occurrence of dangerous situations. In this paper we present a temporal reasoning system for modeling and analyzing various types of accident scenarios. The system is based on Event Calculus and was implemented using Prolog. We exemplify the use of the system by applying it for modeling and analyzing a rear-end accident scenario.

This book presents thoroughly arranged tutorial papers corresponding to lectures given by leading researchers at the Second International Summer School on Reasoning Web in Lisbon, Portugal, in September 2006. Building on the predessor school held in 2005 and published as LNCS 3564, the ten tutorial lectures presented provide competent coverage of current topics in semantic Web research and development.

Ontology matching consists of finding correspondences between se-mantically related entities of two ontologies. OAEI campaigns aim at comparingontology matching systems on precisely defined test cases. These test cases canuse ontologies of different nature (from simple thesauri to expressive OWL on-tologies) and use different modalities, e.g., blind evaluation, open evaluation andconsensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 partici-pants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentationof the OAEI 2014 campaign.

User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of automated alignment algorithms. In this paper we present a broad study on user validation of ontology alignments that encompasses three distinct but interrelated 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 (OAEI) 2015 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.

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.

Ontology design patterns (ODPs) and related ontology development methodologies were designed as ways of sharing and reusing best practices in ontology engineering. However, while the use of these reduces the number of issues in the resulting ontologies defects can still be introduced into the ontology due to improper use or misinterpretation of the patterns. Thus, the quality of the developed ontologies is still a major concern. In this paper we address this issue by describing how ontology debugging and matching can be integrated in a state-of-the-art ontology development methodology based on ontology design patterns- the eXtreme Design methodology, and show the advantages in a case study based on a real world ontology.

Ontologies in the biomedical domain are becoming a key element for data integration and search. The usefulness of the applications which use ontologies is often directly influenced by the quality of ontologies, as incorrect or incomplete ontologies might lead to wrong or incomplete results for the applications. Therefore, there is an increasing need for repairing defects in ontologies. In this paper we focus on completing ontologies. We provide an algorithm for completing the is-a structure in TeX ontologies which covers many biomedical ontologies. Further, we present an implemented system based on the algorithm as well as an evaluation using three biomedical ontologies.

As the quality of ontologies plays an important role in supporting semantically-enabled applications, defining concepts as well as their relations correctly and completely is crucial when developing an ontology. In this paper we introduce a Protégé plugin for extending ontologies, which guides Protégé users through the addition of new concepts as well as their instances and axioms in which they participate in a semi-automatic way. Furthermore, the tool suggests additional subsumption axioms that a user can validate and, if appropriate, add to the ontology to make the ontology more complete.

Current key management schemes for multicast provide either no resistance to collusion or perfect resistance to collusion. However, resistance to collusion is achieved at the expense of efficiency in terms of the number of transmissions and the number of keys that are used. We argue that applications may have certain assumptions regarding the users and their access to the multicast channel that may be used to provide a broader range of choices for balancing efficiency against resistance to collusion.

We formalize the collusion requirement based upon the users' access to the multicast channel. Different user categorizations give different degrees of collusion resistance and we show that the existing work has focused on special cases of user categorizations. Further, we go on to propose and evaluate a flexible key management strategy for the general case where the accessibility relation defines the order of exclusion of the categories. The theoretical and experimental results show that our scheme has good performance regarding transmissions and keys per controller.

Security and efficiency of rekeying are crucial requirements for multicast key management. However, the two requirements pull in different directions and balancing them to meet the application needs is still an open issue. In this paper we introduce a hybrid key tree scheme to balance security, namely the resistance to collusion, and the efficiency. The resistance to collusion is measured by an integer parameter. The communication and the storage requirements for the controller depend on this parameter too, and they decrease as the resistance to collusion is relaxed. We analytically evaluate the efficiency of our scheme and compare with the previous work. The results show that our scheme allows a fine-tuning of security requirements versus efficiency requirements at run-time, which is not possible with the previous key management schemes.

In multicast group communication, efficiency- and security are competing requirements and balancing them is an acknowledged challenge. In particular, the collusion resistance has an impact on the efficiency of any scheme. In this context, the category-based group key management (category-based GKM) scheme balances the collusion resistance constraints against the communication cost and the group controller storage. However, this scheme increases the storage requirements for users. In this paper we address this problem by introducing a novel technique based on spanning hash key tree (SKT). In the worst case, using our t echnique, the storage requirement remains the same as in the original category-based GKM scheme. However, the experimentalresults show that, in general, the SKT technique greatly reduces the key storage for the users as well as for the controller.

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

There are very few published studies regarding the performance of persistence mechanisms for systems that use the openEHR multi level modelling approach. This paper addresses the performance and size of XML databases that store openEHR compliant documents. Database size and response times to epidemiological queries are described. An anonymized relational epidemiology database and associated epidemiological queries were used to generate openEHR XML documents that were stored and queried in four opensource XML databases. The XML databases were considerably slower and required much more space than the relational database. For population-wide epidemiological queries the response times scaled in order of magnitude at the same rate as the number of records (total database size) but were orders of magnitude slower than the original relational database. For individual focused clinical queries where patient ID was specified the response times were acceptable. This study suggests that the tested XML database configurations without further optimizations are not suitable as persistence mechanisms for openEHR-based systems in production if population-wide ad hoc querying is needed.

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Traditionally, theoretical foundations in data structures and algorithms (DSA) courses have been covered through lectures followed by tutorials, where students practise their understanding on pen-and-paper tasks. In this paper, we present findings from a pilot study on using the interactive e-book OpenDSA as the main material in a DSA course. The goal was to redesign an already existing course by building on active learning and continuous examination through the use of OpenDSA. In addition to presenting the study setting, we describe findings from four data sources: final exam, OpenDSA log data, pre and post questionnaires as well as an observation study. The results indicate that students performed better on the exam than during previous years. Students preferred OpenDSA over traditional textbooks and worked actively with the material, although a large proportion of them put off the work until the due date approaches.

Traditionally, theoretical foundations in data structuresand algorithms (DSA) courses have been covered throughlectures followed by tutorials, where students practise theirunderstanding on pen-and-paper tasks. In this paper, we presentfindings from a pilot study on using the interactive e-bookOpenDSA as the main material in a DSA course. The goal was toredesign an already existing course by building on active learningand continuous examination through the use of OpenDSA. Inaddition to presenting the study setting, we describe findings fromfour data sources: final exam, OpenDSA log data, pre- and postcourse questionnaires as well as an observation study. The resultsindicate that students performed better on the exam than duringprevious years. Students preferred OpenDSA over traditionaltextbooks and worked actively with the material, although alarge proportion of them put off the work until the due dateapproaches.

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that  a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.

Predicting game or season outcomes is important for clubs as well as for the betting industry. Understanding the critical factors of winning games and championships gives clubs a competitive advantage when selecting players for the team and implementing winning strategies. In this paper, we work with NBA data from 10 seasons and propose an approach for predicting game outcomes that is then used for predicting which team will be champion and which stages a team will reach in the playoffs. We show that our approach has a similar performance as the odds from betting companies and does better than ELO.

Ontology alignment is an area of active research where many algorithms and approaches are being developed. Their performance is usually evaluated by comparing the produced alignments to a reference alignment in terms of precision, recall and F-measure. These measures, however, only provide an overall assessment of the quality of the alignments, but do not reveal differences and commonalities between alignments at a finer-grained level such as, e.g., regions or individual mappings. Furthermore, reference alignments are often unavailable, which makes the comparative exploration of alignments at different levels of granularity even more important. Making such comparisons efficient calls for a “human-in-the-loop” approach, best supported through interactive visual representations of alignments. Our approach extends a recent tool, Matrix Cubes, used for visualizing dense dynamic networks. We first identify use cases for ontology alignment evaluation that can benefit from interactive visualization, and then detail how our Alignment Cubes support interactive exploration of multiple ontology alignments. We demonstrate the usefulness of Alignment Cubes by describing visual exploration scenarios, showing how Alignment Cubes support common tasks identified in the use cases.

With the increased use of ontologies in semantically-enabled applications,the issues of debugging and aligning ontologies have become increasinglyimportant. The quality of the results of such applications is directly dependent onthe quality of the ontologies and mappings between the ontologies they employ. Akey step towards achieving high quality ontologies and mappings is discoveringand resolving modeling defects, e.g., wrong or missing relations and mappings.In this demonstration paper we present a system for aligning taxonomies, the mostused kind of ontologies, and debugging taxonomies and their alignments, whereontology alignment is treated as a special kind of debugging.