Definition Of Ontology Research Articles

What is spirituality? The challenges of a philosophical definition

AbstractIn recent years, there have been a number of philosophical publications focusing on spirituality. But even in pertinent philosophical texts it is rare to find attempts at shaping a workable definition of spirituality, despite the obvious need for a clear definition for the philosophical debate on spirituality. This paper addresses the major issues in shaping a satisfactory definition of spirituality: an understanding of spirituality as transcendence of critical reasoning, the broadness of the concept, the implications of various scholarly backgrounds and the challenge of avoiding a circular definition. It suggests a rough clustering of common approaches into ontological, ethical, and functional definitions and discusses their weaknesses. It is argued that an inclusive, metaphysically parsimonious, and stipulative definition is the best way to address typical difficulties and avoid weaknesses of common definitions. Finally, it proposes a definition based on meaning, a term that often appears in the discussion of spirituality. It is suggested that understanding meaning in the ordinary sense of the word might be a good starting point to arrive at a satisfactory definition of spirituality.

Conceptual analysis of sensitive business processes

PurposeThis paper introduces the problematic of the SBP modeling. Our objective is to provide a conceptual analysis related to the concept of SBP. This facilitates, on the one hand, easier understanding by business analysts and end-users, and one the other hand, the integration of the new specific concepts relating to the SBP/BPM-KM domains into the BPMN meta-model (OMG, 2013).Design/methodology/approachWe propose a rigorous characterization of SBP (Sensitive Business Processes) (which distinguishes it from classic, structured and conventional BPs). Secondly, we propose a multidimensional classification of SBP modeling aspects and requirements to develop expressive, comprehensive and rigorous models. Besides, we present an in-depth study of the different modeling approaches and languages, in order to analyze their expressiveness and their abil-ity to perfectly and explicitly represent the new specific requirements of SBP modeling. In this study, we choose the better one positioned nowadays, BPMN 2.0, as the best suited standard for SBP representation. Finally, we propose a semantically rich conceptualization of a SBP organized in core ontology.FindingsWe defined a rigorous conceptual specification for this type of BP, organized in a multi-perspective formal ontology, the Core Ontology of Sensitive Business Processes (COSBP). This reference ontology will be used to define a generic BP meta-model (BPM4KI) further specifying SBPs. The objective is to obtain an enriched consensus modeling covering all generic concepts, semantic relationships and properties needed for the exploitation of SBPs, known as core modeling.Originality/valueThis paper introduces the problem of conceptual analysis of SBPs for (crucial) knowledge identification and management. These processes are highly complex and knowledge-intensive. The originality of this contribution lies in the multi-dimensional approach we have adopted for SBP modeling as well as the definition of a Core Ontology of Sensitive Business Processes (COSBP) which is very useful to extend the BPMN notation for knowledge management.

”CITYJSON2RDF” A CONVERTER FOR PRODUCING 3D CITY KNOWLEDGE GRAPHS

Abstract. The increasing prevalence of 3D city models (3DCMs) in various applications, such as mixed reality and navigation, highlights the need for efficient data exchange. CityGML serves as a standard model for this purpose, encompassing geometric and semantic information in 3DCM data. To enhance interoperability, a compatible transfer mechanism is essential. This study introduces a conversion tool that transforms CityGML data into RDF, a knowledge graph (KG) format. Utilizing semantic web technologies, this conversion ensures the data’s seamless integration across applications. The RDF model facilitates linking to open ontologies, promoting data circulation without loss. The tool employs CityJSON encoding for its mappable JSON structure, enabling straight-forward conversion to RDF using Python components. While existing XML to RDF tools exist, this tool distinguishes itself by addressing accessibility and user intervention challenges. Linking is established by matching subject classes with relevant ontology definitions, a process dependent on developers’ understanding of the CityGML data model. The tool, accessible through URL-2, is still in development, offering a promising solution for achieving effective 3DCM data interoperability.

The Future of School Health Education in the United States: An Ontology.

As summarized in this article, the widespread implementation of modern school health education (SHE) could become one of the most effective means available to improve the well-being of people in the United States and in other nations. However, the development and evolution of SHE largely remains unorganized, underdeveloped, and neglected by health and education agencies, policymakers, and the public. Essential to the development of any scientific discipline, scientists today use the word ontology to refer to efforts to organize knowledge in particular domains. A useful working definition of a scientific ontology is an explicit, formal specification of a shared conceptualization-a systematic set of shared terms and an explication of their interrelationships. Nine interdependent questions are outlined to help guide the development of an initial, broad, and actionable scientific ontology for SHE. Whether and how we respond to these questions arguably will determine the future of SHE research, policy, practice, and equity in the United States. An initial ontology might help conceptualize, inform, and facilitate more systematic and strategic local, state, national, and international deliberations and actions to improve SHE.

Challenges of the current discourse on incumbent firms in sustainability transitions

In this viewpoint article, I argue that, as the view on incumbent firms has broadened over the years, a number of challenges have emerged that hamper the discourse on the role of incumbents in sustainability transitions and the advancement of theoretical knowledge. These challenges arise from (1) different – often implicit – definitions of incumbents and reference ontologies; (2) insufficient consideration of sectoral boundaries and inter-sectoral dynamics; (3) unclear criteria for the interpretation of the role of incumbents for the sustainability transition process under study; (4) the non-consideration of capitalism and capitalism theory when analysing the role of incumbent firms in sustainability transitions. I illustrate these challenges with examples from transition research and formulate potential solutions.

SPARQL-QA-v2 system for Knowledge Base Question Answering

Accessing the large volumes of information available in public knowledge bases might be complicated for those users unfamiliar with formal languages, such as the SPARQL query language and the ontology definition languages. This issue can be overcome by providing systems able to answer questions posed in natural language on a knowledge base, a task that is called Knowledge Base Question Answering (KBQA) in the literature. More in detail, many KBQA systems aim at translating automatically questions into the corresponding SPARQL queries to be executed over the knowledge base to get the answers. Effective state-of-the-art KBQA systems are based on neural-machine translation but easily fail to recognize words that are Out Of the Vocabulary (OOV) of the training set. This is a serious issue while querying large ontologies where the list of entities is huge and easily evolves over time. In this paper, we present the SPARQL-QA-v2 system that combines in an innovative way Named Entity Linking, Named Entity Recognition, and Neural Machine Translation for addressing the problem of generating SPARQL queries from questions posed in natural language. We demonstrate empirically that SPARQL-QA-v2 is effective and resilient to OOV words and delivers state-of-the-art performance in well-known datasets for question answering over DBpedia and Wikidata knowledge bases.

Development and validation of the early warning system scores ontology

BackgroundClinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems.MethodsWe apply the Software Development Lifecycle Framework—conceived by Winston Boyce in 1970—to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building.ResultsWe developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time.ConclusionsUsing synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.

A Knowledge Graph-based knowledge representation for adaptive manufacturing control under mass personalization

Mass personalization is an achievable manufacturing paradigm, which requires flexible and responsible manufacturing operations in response to dynamic batch sizes of personalized products. A Self-Organizing Manufacturing Network (SOMN) has been proposed to achieve mass personalization. A crucial aspect of SOMN is adaptive manufacturing control, and the Knowledge Graph, a powerful tool, has been recognized as a promising solution to enhance manufacturing intelligence. However, the current Knowledge Graph research mainly focuses on the modeling and ontology definition of the manufacturing environment, but neglects the interaction between manufacturing resources, the dynamic features of the manufacturing environment, and the application of the Knowledge Graph towards adaptive manufacturing control. Therefore, this paper proposes a Knowledge Graph-based semantic representation for adaptive manufacturing control under dynamic manufacturing environments. The proposed approach develops the Knowledge Graph based on historical and real-time scheduling data. Based on the established Knowledge Graph, Multi-Agent Reinforcement Learning has been introduced as an illustrative example of achieving adaptive scheduling control.

Back by popular demand, ontology

In this paper we analyze relations between ontology in anthropology and philosophy beyond simple homonymy or synonymy and show how this diagnosis allows for new interdisciplinary links and insights, while minimizing the risk of cross-disciplinary equivocation. We introduce the ontological turn in anthropology as an intellectual project rooted in the critique of dualism of culture and nature and propose a classification of the literature we reviewed into first-order claims about the world and second-order claims about ontological frameworks. Next, rather than provide a strict definition of ontology in anthropological literature, we argue that the term is used as a heuristic addressing a web of sub-concepts relating to interpretation, knowledge, and self-determination which correspond to methodological, epistemic, and political considerations central to the development of the ontological turn. We present a case study of rivers as persons to demonstrate what the ontological paradigm in anthropology amounts to in practice. Finally, in an analysis facilitated by a parallel between the first- and second-order claims in anthropology, and ontology and meta-ontology in philosophy (respectively), we showcase the potential for contribution of ontological anthropology to contemporary philosophical debates, such as ontological gerrymandering, relativism and social ontology, and vice versa.

An Аxiomatic System of Philosophical Ontology

For the first time in the history of philosophy, this article presents an axiomatic system of philosophical ontology intending to demonstratively solve “eternal” questions related to the most fundamental problems of human cognition of the natural (material) world. The corresponding system of axioms and definitions of philosophical ontology is proposed. Proofs of the first 20 theorems of axiomatic ontology are presented as solutions to traditional and modern aporias (incompatible alternatives) of ontology. Finally, the principles and most general laws of modern physics, including the Microworld and cosmology, are used as an empirical base of the theoretical system of philosophical ontology.

The Spacialization Revisited: Extensive Type’s Characterization and Contradictions in Theodor W. Adorno

As Adorno’s posthumous writings were released, a notable turn in his philosophy of music became noteworthy: by revealing an ambitious project on Beethoven, a quasi-axiomatic theory of musical time emerged, which seems to guide adornian thought at very significant works and texts, as Philosophy of New Music. Sketched in three configuration types of musical time, this theory surprises with the so-called “extensive type”. Adorno would then admit kinds of formal consistency (Stimmigkeit) that are not restricted to the ideal of development (Entwicklung) and assumes the possibility of a non-regressive spatialization, which contrasts with his own ontological definition of the medium. This paper discusses the contradictions of extensive type by presenting an analysis of the Archduke Trio op. 97, in the light of the well-known originary affinity between Beethoven and Hegel. At last, the text presents by which means and for what reasons the extensive type would lead Adorno to the last and most decisive modality of musical time’s configuration, the late style.

The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research

The paper concerns the definition of a novel ontology for cultural heritage based on the concept of digital twin. The ontology, called Heritage Digital Twin ontology, is a compatible extension of the well-known CIDOC CRM ISO standard for cultural heritage documentation and incorporates all the different documentation systems presently in use for cultural heritage documentation. In the authors’ view, it supports documentation interoperability at a higher level than the ones currently in use and enables effective cooperation among different users.

Methods of managing the evolution of ontologies and their alignments

Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements.

CYBER-AIDD: A novel approach to implementing improved cyber security resilience for large Australian healthcare providers using a Unified Modelling Language ontology.

This paper proposes a novel cyber security risk governance framework and ontology for large Australian healthcare providers, using the structure and simplicity of the Unified Modelling Language (UML). This framework is intended to mitigate impacts from the risk areas of: (1) cyber-attacks, (2) incidents, (3) data breaches, and (4) data disclosures. Using a mixed-methods approach comprised of empirical evidence discovery and phenomenological review, existing literature is sourced to confirm baseline ontological definitions. These are supplemented with Australian government reports, professional standards publications and legislation covering cyber security, data breach reporting and healthcare governance. Historical examples of healthcare cyber security incidents are reviewed, and a cyber risk governance UML presented to manage the defined problem areas via a single, simplified ontological diagram. A clear definition of 'cyber security' is generated, along with the 'CYBER-AIDD' risk model. Specific examples of cyber security incidents impacting Australian healthcare are confirmed as N = 929 over 5 years, with human factors the largest contributor. The CYBER-AIDD UML model presents a workflow across four defined classes, providing a clear approach to implementing the controls required to mitigate risks against verified threats. The governance of cyber security in healthcare is complex, in part due to a lack of clarity around key terms and risks, and this is contributing to consistently poor operational outcomes. A focus on the most essential avenues of risk, using a simple UML model, is beneficial in describing these risks and designing governance controls around them.

ON THE POSSIBILITY OF A RATIONAL CONCEPT OF ABSOLUTE NON-BEING

The subject of research is the problem of non-existence, or rather absolute nothingness. This is one of the central ontological problems as it is, in essence, one of the formulations of the first principle problem.This paper directly addresses the problem of nothingness from the perspective of the approach that identi-fies itself as a «philosophy of nothingness». According to this approach, substance is claimed to be abso-lute nothingness, which gives rise to existence. Currently, there can be distinguished three varieties in the «philosophy of nothingness»: rationalistic (dialectical), irrational (metaphysical), and radical (absolute, total). The immediate objective of this study is to analyze the possibility of identifying absolute nothing-ness by rational means. This possibility is declared by a rationalist version of the «philosophy of nothing-ness». The problem is considered in the paper from the point of view of dialectical methodology, which is also used in rationalist «philosophy of nothingness». The study shows the difficulty of substantializationof nothingness. The author provides arguments for the impossibility of a consistent rational concept of absolute nothingness. It is concluded that the problem of the relationship between existence and non-existence can be rationally solved only through consistent dialectical methodology. This methodology denies substantialist obscurity and implies a substantialist dialectical relationship between being and noth-ingness as the essential characteristics of the first principle. A positive value of the «philosophy of noth-ingness» is that draws attention to the ontological study of the first principle, makes it possible to look beyond the traditional understanding of this problem. The study results can be helpful in the search for a universally accepted ontological definition of matter.

Intelligent information retrieval based on Ontology

Traditional search engines can no longer meet the increasing amount of information in today's world, and often face problems such as blurred results and long search times for some queries. And the addition of ontology makes information retrieval more intelligent, so that information data between various fields can be shared, to achieve the purpose of getting the answers users want quickly and accurately. This paper firstly introduces the definition of ontology and analyzes the traditional information retrieval system, and finally summarizes some experimental results in recent years in ontology-based intelligent information retrieval and reviews them to get a more efficient and accurate information retrieval system to achieve the effect of improving the accuracy of search engines.

Sustainable Risk Identification Using Formal Ontologies

The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

The scientific literature is a valuable source of information for developing predictive models to design decision support systems. However, scientific data are heterogeneously structured expressed using different vocabularies. This study developed a generic workflow that combines ontology, databases and computer calculation tools based on the theory of belief functions and Bayesian networks. The ontology paradigm is used to help integrate data from heterogeneous sources. Bayesian network is estimated using the integrated data taking into account their reliability. The proposed method is unique in the sense that it proposes an annotation and reasoning tool dedicated to systematic analysis of the literature, which takes into account expert knowledge of the domain at several levels: ontology definition, reliability criteria and dependence relations between variables in the BN. The workflow is assessed successfully by applying it to a complex food engineering process: skimmed milk microfiltration. It represents an original contribution to the state of the art in this application domain.

Copying safety and liveness properties of computational artefacts

Abstract This paper shows how safety and liveness properties are not necessarily preserved by different kinds of copies of computational artefacts and proposes procedures to preserve them, which are consistent with ethical analyses on software property rights infringement. Safety and liveness are second-order properties that are crucial in the definition of the formal ontology of computational artefacts. Software copies are analysed at the level of their formal models as exact, inexact and approximate copies, according to the taxonomy in [3]. First, it is explained how exact copies are the only kind of copies that preserve safety and liveness properties, and how inexact and approximate copies do not necessarily preserve them. Secondly, two model checking algorithms are proposed to verify whether inexact and approximate copies actually preserve safety and liveness properties. Essential properties of termination, correctness and complexity are proved for these algorithms. Finally, contraction and expansion algorithmic operations are defined, allowing for the automatic design of safety- and liveness-preserving approximate copies. As a conclusion, the relevance of the present logical analysis for the ongoing debates in miscomputation and computer ethics is highlighted.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BackgroundIn times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.ResultsWe argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.ConclusionsWe conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.