Search

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.

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.

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.

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.

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.

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.

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.