‘Thinking Ontologically’: Ontology Development as the Basis for the Systematic Analysis of a Corpus of Late Antique Aramaic Magic Texts

This article describes the process of developing an ontology of the domain of Jewish Babylonian Aramaic magic bowls and offers some reflections on its significance in the analysis of these materials. Examples are highlighted to illustrate where the work builds on existing conceptualisations of the domain in secondary literature and where magical and religious materials from the Ancient Near East might stimulate some specialised extension of the CIDOC Conceptual Reference Model (ICOM/CIDOC Documentation Standards Group, 2020). The analogy of ‘bridge building’ is offered as a way for humanities researchers to conceive of the work to produce ontologies of specific domains. This reflection is intended to capture the experience of ‘thinking ontologically’ about sources for the first time and of overcoming misconceptions about the nature and significance of this work.

Construction safety ontology development and alignment with industry foundation classes (IFC)

A pronounced gap often exists between expected and actual safety performance in the construction industry. The multifaceted causes of this performance gap are resulting from the misalignment between design assumptions and actual construction processes that take place on-site. In general, critical factors are rooted in the lack of interoperability around the building and work-environment information due to its heterogeneous nature. To overcome the interoperability challenge in safety management, this paper represents the development of an ontological model consisting of terms and relationships between these terms, creating a conceptual information model for construction safety management and linking that ontology to IfcOWL. The developed ontology, named Safety and Health Exchange (SHE), comprises eight concepts and their relationships required to identify and manage safety risks in the design and planning stages. The main concepts of the developed ontology are identified based on reviewing accident cases from 165 Reporting of Injuries, Diseases and Dangerous Occurrences Regulations (RIDDOR) and 31 Press Releases from the database of the Health and Safety Executive (HSE) in the United Kingdom. Consequently, a semantic mapping between the developed ontology and IfcOWL (the most popular ontology and schema for interoperability in the AEC sector) is proposed. Then several SPARQL queries were developed and implemented to evaluate the semantic consistency of the developed ontology and the cross-mapping. The proposed ontology and cross-mapping gained recognition for its innovation in utilising OpenBIM and won the BuildingSMART professional research award 2020. This work could facilitate developing a knowledge-based system in the BIM environment to assist designers in addressing health and safety issues during the design and planning phases in the construction sector.

An Infrastructure for Collaborative Ontology Development

Collaborative development of a shared or standardized ontology presents unique issues in workflow, version control, testing, and quality control. These challenges are similar to challenges faced in large-scale collaborative software development. We have taken this idea as the basis of a collaborative ontology development platform based on familiar software tools, including Continuous Integration platforms, version control systems, testing platforms, and review workflows. We have implemented these using open-source versions of each of these tools, and packaged them into a full-service collaborative platform for collaborative ontology development. This platform has been used in the development of FIBO, the Financial Industry Business Ontology, an ongoing collaborative effort that has been developing and maintaining a set of ontologies for over a decade. The platform is open-source and is being used in other projects beyond FIBO. We hope to continue this trend and improve the state of practice of collaborative ontology design in many more industries.

A Bottom-up Ontology-based Approach to Monitor Circular Economy: Aligning User Expectations, Tools, Data and Theory

With circular economy being high on governmental agendas, there is an increasing request from governing bodies for circularity measurements. Yet currently existing macro-level monitoring frameworks are widely criticized for not being able to inform the decision making. The reasons behind their failure stem from a lack of consensus on terminologies and definitions among scholars, politicians and practitioners, a lack of supporting data and tools and, consequently, a lack of transparency and trustworthiness.To fulfill those needs, a bottom-up approach to build a shared terminology is suggested by involving macro-framework users within a government, data providers and tool developers. Their expertise and expectations for monitoring the transition are elicited through the process of formal ontology development and alignment.The ontology development experiment builds upon a use case of the Amsterdam Circular Economy Monitor (2020). First, four ontology development approaches are used to create a theory-centered, a user-centered, a tool-centered and a data-centered ontology. The ontologies are later compared, merged, and aligned with each other to arrive at one single ontology. The notes taken during the process are used to provide a detailed discussion on common concepts, identified conflicts, and gaps in monitoring expectations between the monitor users, data, tools, and the latest theory.

RDFizing the biosynthetic pathway of E.coli O-antigen to enable semantic sharing of microbiology data

Background The abundance of glycomics data that have accumulated has led to the development of many useful databases to aid in the understanding of the function of the glycans and their impact on cellular activity. At the same time, the endeavor for data sharing between glycomics databases with other biological databases have contributed to the creation of new knowledgebases. However, different data types in data description have impeded the data sharing for knowledge integration. To solve this matter, Semantic Web techniques including Resource Description Framework (RDF) and ontology development have been adopted by various groups to standardize the format for data exchange. These semantic data have contributed to the expansion of knowledgebases and hold promises of providing data that can be intelligently processed. On the other hand, bench biologists who are experts in experimental finding are end users and data producers. Therefore, it is indispensable to reduce the technical barrier required for bench biologists to manipulate their experimental data to be compatible with standard formats for data sharing. Results There are many essential concepts and practical techniques for data integration but there is no method to enable researchers to easily apply Semantic Web techniques to their experimental data. We implemented our procedure on unformatted information of E.coli O-antigen structures collected from the web and show how this information can be expressed as formatted data applicable to Semantic Web standards. In particular, we described the E-coli O-antigen biosynthesis pathway using the BioPAX ontology developed to support data exchange between pathway databases. Conclusions The method we implemented to semantically describe O-antigen biosynthesis should be helpful for biologists to understand how glycan information, including relevant pathway reaction data, can be easily shared. We hope this method can contribute to lower the technical barrier that is required when experimental findings are formulated into formal representations and can lead bench scientists to readily participate in the construction of new knowledgebases that are integrated with existing ones. Such integration over the Semantic Web will enable future work in artificial intelligence and machine learning to enable computers to infer new relationships and hypotheses in the life sciences.

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

Ontologies application in the sharing economy domain: a systematic review

PurposeThis systematic literature review (SLR) paper presents the overview and analysis of the existing ontologies application in the SE domain. It discusses the main challenges in terms of its ontologies development and highlights the key knowledge areas for subdomains in the SE domain that provides a direction to develop ontologies application for SE systematically. The SE is not as straightforward as the traditional economy. It transforms the existing economy ecosystem through peer-to-peer collaborations mediated by the technology. Hence, the complexity of the SE domain accentuates the need to make the SE domain knowledge more explicit.Design/methodology/approachFor the review, the authors only focus on the journal articles published from 2010 to 2020 and mentioned ontology as a solution to overcome the issues specific for the SE domain. The initial identification process produced 3,326 papers from 10 different databases.FindingsAfter applying the inclusion and exclusion criteria, a final set of 11 articles were then analyzed and classified. In SE, good ontology design and development is essential to manage digital platforms, deal with data heterogeneity and govern the interoperability of the SE systems. Yet the preference to build an application ontology, lack of perdurant design and minimal use of the existing standard for building SE common knowledge are deterring the ontology development in this domain. From this review, an anatomy of the SE key subdomain areas is visualized as a reference to further develop the domain ontology for the SE domain systematically.Originality/valueWith the arrival of the Fourth Industrial Revolution (4IR), the sharing economy (SE) has become one of the important domains whose impact has been explosive, and its domain knowledge is complex. Yet, a comprehensive overview and analysis of the ontology applications in the SE domain is not available or well presented to the research community.

Ontologia aplicada à redução de ruído em base de dados de tweets sobre mercado financeiro

Big data é um conceito que trata sobre a manipulação e a análise de grandes volumes de dados de variedade diversa. A rede social Twitter é uma fonte de dados com tais características, responsável por gerar milhões de tweets por dia. Os mecanismos que permitem a extração dessas postagens resultam em bases de dados heterogêneas, isto é, compostas não apenas por textos sobre o tema de interesse, mas também sobre tópicos indesejados, o que prejudica o uso dessas bases de dados à tomada de decisão. Nesse contexto, o artigo propõe o desenvolvimento de uma ontologia de domínio para a redução de ruídos em base de dados de tweets para o mercado financeiro brasileiro. A ontologia desenvolvida deve ser capaz de identificar tweets, escritos em língua portuguesa, relacionados à Bolsa de Valores do Brasil e descartar publicações da rede social que não pertencem a esse domínio (ruídos). Devido à natureza informal dos textos da rede social, foram utilizadas técnicas tradicionais de pré-processamento textual. A ontologia foi criada com o auxílio de um roteiro que une as metodologias On-to-Knowledge, Methontology e o guia Ontology Development 101. Além disso, para avaliar a performance da filtragem, foi utilizado um algoritmo de classificação simples, a Regressão Logística. A base de dados utilizada neste trabalho é composta por 1.031.419 tweets, que foram publicados entre 01 de janeiro de 2019 e 12 de junho de 2019. Os resultados demonstram que o uso de ontologia para filtragem desses ruídos é promissor, tendo em vista que obteve acurácia de 81,58%.