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Reusing ontologies for new purposes, or adapting them to new use-cases, is frequently difficult. In our experiences, we have found this to be the case for several reasons: (i) differing representational granularity in ontologies and in use-cases, (ii) lacking conceptual clarity in potentially reusable ontologies, (iii) lack and difficulty of adherence to good modeling principles, and (iv) a lack of reuse emphasis and process support available in ontology engineering tooling. In order to address these concerns, we have developed the Modular Ontology Modeling (MOMo) methodology, and its supporting tooling infrastructure, CoModIDE (the Comprehensive Modular Ontology IDE - "commodity"). MOMo builds on the established eXtreme Design methodology, and like it emphasizes modular development and design pattern reuse; but crucially adds the extensive use of graphical schema diagrams, and tooling that support them, as vehicles for knowledge elicitation from experts. In this paper, we present the MOMo workflow in detail, and describe several useful resources for executing it. In particular, we provide a thorough and rigorous evaluation of CoModIDE in its role of supporting the MOMo methodology's graphical modeling paradigm. We find that CoModIDE significantly improves approachability of such a paradigm, and that it displays a high usability.

The amount of information on evidence-based design in lighting is mounting, however the facility managers, lighting designers, and researchers are struggling with an implementation of an efficient post-occupancy evaluation system. This research aims to establish a method for Digital Occupancy Assessment for Lighting Evaluation (DOALE) to strengthen evidence-based lighting research supporting value generation for stakeholders. A prototype has been implemented using Azure Digital Twin and the RealEstateCore ontology to investigate how an ontology and a commercial platform, developed to facilitate data integration for smart buildings, can be used for occupancy assessment. The prototype was evaluated using a post occupancy evaluation session where data concerning environmental factors (temperature, illuminance) and data concerning occupants (motion, galvanic skin response, and heart rate) were gathered. Questionnaires concerning occupancy perception were also carried out using mobile technology. Several interviews/discussions were also conducted with the lighting researchers using the prototype to perform the POE. The result indicates that it is possible to use current commercial digital twin technology to implement a post-occupancy evaluation system, but the technology is at present so complex that it is hard for a lighting researcher to adopt the system.

Ontologies are the corner stone of data modeling and knowledge representation, and engineering an ontology is a complex task in which domain knowledge, ontological accuracy and computational properties need to be carefully balanced. As with any engineering task, the identification and documentation of common patterns is important, and Ontology Design Patterns (ODPs) provide ontology designers with a strong connection to requirements and a better communication of their semantic content and intent. This book, Advances in Pattern-Based Ontology Engineering, contains 23 extended versions of selected papers presented at the annual Workshop on Ontology Design and Patterns (WOP) between 2017 and 2020. This yearly event, which attracts a large number of researchers and professionals in the field of ontology engineering and ontology design patterns, covers issues related to quality aspects of ontology engineering and ODPs for data and knowledge representation, and is usually co-located with the International Semantic Web Conference (ISWC), apart from WOP 2020, which was held virtually due to the COVID-19 pandemic. Topics covered by the papers collected here focus on recent advances in ontology design and patterns, and range from a method to instantiate content patterns, through a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations and applications. The book provides an overview of important advances in ontology engineering and ontology design patterns, and will be of interest to all those working in the field.

Since 2018, Wikidata has had the ability to describe lexemes, and the associated SPARQL endpoint Wikidata Query Service can query this information and visualize the results. Ordia is a Web application that displays the multilingual lexeme data of Wikidata based on embedding of the responses from the Wikidata Query Service via templated SPARQL queries. Ordia has also a SPARQL-based approach for online matching of the words of a text with Wikidata lexemes and the ability to use a knowledge graph embedding as part of a SPARQL query. Ordia is available from https://tools.wmflabs.org/ordia/.

In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects. The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

Syfte Syftet är att undersöka uppskattade ledarbeteenden och ledaregenskaper hos manliga respektive kvinnliga fotbollsspelare, samt observera ledarbeteenden hos tränare för manliga respektive kvinnliga fotbollslag. Metod Studien utgick från mixed methods. Metoden bestod av enkäter som fotbollsspelare besvarade genom rangordning av tio ledarbeteenden och tio ledaregenskaper och öppna frågor med motivering. Observationer utfördes på fem huvudtränare med hjälp av Arizona State Observation Instrument (ASOI) samt övriga anteckningar. Resultat Ärlig och Ställa krav var mest uppskattade ledarbeteenden hos herrlagen. Engagerande och Rättvis var herrlagens mest uppskattade ledaregenskaper. Socialt stöd samt Lugn var herrlagens minst uppskattade ledarbeteenden. Tidigare erfarenhet och Medvetet jobba skadeförebyggande var herrlagens minst uppskattade ledaregenskaper. Feedback, Instruerande och Positiv och social var damlagens mest uppskattade ledarbeteenden. Engagerande, Förmåga att lära ut och Utveckla spelare för att ta nästa steg var damlagens mest uppskattade ledaregenskaper. Socialt stöd och Öppen med känslor var damlagens minst uppskattade ledarbeteenden. Emotionell intelligens och Tidigare erfarenhet var damlagens minst uppskattade ledaregenskaper. ”Feedback under övning utan paus” samt ”Verbala ord, få dem att göra / jobba bättre” var de mest frekventa ledarbeteenden som observerades hos herrlagen. ”Feedback under övning utan paus” samt ”Beröm” var de ledarbeteenden som observerade sig hos de kvinnliga lagens huvudtränare. Från övriga anteckningar framgick många skillnader på ledarbeteenden mellan herr och damlagen. Slutsatser Studiens slutsats visar att herrlagen rangordnar en målinriktade ledare högst till skillnad från damlagen som uppskattar en blandning av målinriktade ledare och relationsinriktade ledare. Likheter och skillnader framgick mellan herr / damlagen om vilka ledarbeteenden och ledaregenskaper som uppskattades mest / minst. Herrlagens och damlagens huvu, Purpose The aims of this study where to investigate appreciated leadership behaviors and leadership characteristics at male and female football players and observe leadership behaviors at coaches in male and female football teams. Methods The study emanated from mixed methods. The method consisted of surveys that football players participated in through the ranking of ten leadership behaviors and ten leadership characteristics and open questions to motivate. Observations were performed at five football coaches by using the Arizona State Observation Instrument (ASOI) and other notes. Results Honest and Instructing where the most appreciated leadership behavior at the men’s teams. Engaging and Fair was the most appreciated leadership characteristic at the men’s teams. Social support as well as Calm was the least appreciated leadership behavior at the men’s teams. Previous experiences and Conscious work injury prevention was the least appreciated leadership characteristic at the men’s teams. Feedback, Instructing and Positive and social was the most appreciated leadership behavior at the ladies’ teams. Engaging, Ability to teach and Developing players for next step was the most appreciated leadership characteristic at the ladies’ teams. Social support and Open with feelings was the least appreciated leadership behavior at the ladies’ teams. Emotional intelligence and Previous experience was where the least appreciated leadership characteristic at the ladies’ teams. Feedback during exercise without breaks and Verbal words, make them do / work better, was the most observant leadership behaviors of the coaches from the men’s teams. Feedback during exercise without breaks and Praise was the leadership behavior who were observed at the female team’s head coaches. Other notes showed big differences in leadership behaviors between the men’s and the ladies’ teams head coaches. Conclusions The study’s conclusions showed that the men’s teams estimate more goal orientated leaders

Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts. This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives. The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inex