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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.

The evaluation of explanation methods has become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Explanations are essential for bias detection and control of uncertainty since most accurate AI models are opaque with low transparency and comprehensibility. There are numerous criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality., This research is partly founded by the Swedish Knowledge Foundation through the Industrial Research School INSiDR.

The evaluation of explanation methods has become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Explanations are essential for bias detection and control of uncertainty since most accurate AI models are opaque with low transparency and comprehensibility. There are numerous criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality., This research is partly founded by the Swedish Knowledge Foundation through the Industrial Research School INSiDR.

Explanation methods and their evaluation have become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Since the most accurate AI models are opaque with low transparency and comprehensibility, explanations are essential for bias detection and control of uncertainty. There are a plethora of criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. This lack of consensus creates a critical shortage of rigour in the field, although little is written about comparative evaluations of explanation methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality., Comment: 15 pages, 4 figures, 2 tables, conference article

The evaluation of explanation methods has become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Explanations are essential for bias detection and control of uncertainty since most accurate AI models are opaque with low transparency and comprehensibility. There are numerous criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

The use of Semantic Web-based Technologies (SWT) to support digital Facilities Management (FM) activities has been shown to address interoperability challenges between disciplinary stakeholders. By establishing shared understanding through ontologies, eliminating precarious file exchanges and democratising participation through non-proprietary technologies, SWTs are receiving growing interest from the research community. Despite this, no comprehensive review exists which analyses works with a specific focus on the FM domain. This paper reviews 35 academic works and provides a broad discussion around academic and industry initiatives in SWTs for the FM domain, identifying research gaps and future directions of interest. We find that SWTs are already being used by FM practitioners and that implementation is highly case-specific and thus, developments need to be flexible and user-oriented in their design. This work towards a comprehensive domain review provides a useful reference for others in the field as well as informing our own future research activities.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

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.

This position paper presents a set of design principles for ontology engineering for model-driven applications. Ontologies sometimes need to be translated into less expressive languages and be used by software developers with limited ontology experience. In such scenarios, one may wish to refrain from using OWL features or design patterns that increase interpretation or software implementation complexity. I introduce practical considerations inherent in those scenarios, and discuss their modeling consequences.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Constructing an RDF-based knowledge graph requires designing a data model (typically an OWL ontology) and transforming one’s data into an RDF representation that is compliant with said model. This paper introduces ExcelRDF, a plugin for Microsoft Excel that is intended to simplify the latter task. ExcelRDF can translate an OWL ontology into an Excel skeleton file (empty apart from column headers). It can then, once that skeleton file has been filled out with data, translate it back into an RDF graph representation. ExcelRDF is designed to be simple to install and use, and to work with existing Excel-based data management workflows.

Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

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., Funding agencies: Swedens innovation agency (VINNOVA)

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., Funding agencies: Swedens innovation agency (VINNOVA)

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

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., Funding agencies: Swedens innovation agency (VINNOVA)

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

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.