Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism

Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach

Comprehending 3D and 4D ontology-driven conceptual models: An empirical study

This paper presents an empirical study that investigates the extent to which the pragmatic quality of ontology-driven models is influenced by the choice of a particular ontology, given a certain understanding of that ontology. To this end, we analyzed previous research efforts and distilled three hypotheses based on different metaphysical characteristics. An experiment based on two foundational ontologies (UFO and BORO) involving 158 participants was then carried out, followed by a protocol analysis to gain further insights into the results of experiment. We then extracted five derivations from the results of the empirical study in order to summarize our findings. Overall, the results confirm that the choice of a foundational ontology can lead to significant differences in the interpretation and comprehension of the conceptual models produced. Moreover, the effect of applying a certain foundational ontology can cause considerable variations in the effort required to comprehend these models

A comparative illustration of foundational ontologies : BORO and UFO

This paper investigates the differences that exist between a 3D and a 4D ontology. We examine these differences by comparing both ontologies through the metaphysical choices each ontology makes and explore the composing characteristics that define them. More specifically, the differences between the ontologies were illustrated through several modeling fragments that were derived from a modeling case presented at the 5thOntoCom workshop. Each of these modeling fragments focused on the metaphysical choices that the ontologies make –Essence and Identity, Relationships and Time. These comparisons highlighted the different ontological approaches and structures that exist between the ontologies. Moreover, depending on the ontology, the resulting conceptual model could differ substantially, confirming the impact and importance of the choice of a certain ontology. The observed differences between both ontologies eventually led us to formulate three discussion points that question the applicability of certain metaphysical choices in certain circumstances, and that can serve as a basis for future discussion or future research studies in the domain of ODCM

Exploiting a perdurantist foundational ontology and graph database for semantic data integration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The view of reality that is inherent to perdurantist philosophical ontologies, often termed four dimensional (4D) ontologies, has not been widely adopted within the mainstream of information system design practice. However, as the closed world of enterprise systems is opened to Internet scale Semantic Web and Open Data information sources, there is a need to better understand the semantics of both internal and external data and how they can be integrated. Philosophical foundational ontologies can help establish this understanding and there is, therefore, an emerging need to research how they can be applied to the problem of semantic data integration. Therefore, a prime objective of this research was to develop a framework through which to apply a 4D foundational ontology and a graph database to the problem of semantic data integration, and to assess the effectiveness of the approach. The research employed design science, a methodology which is applicable to undertaking research within information systems as it encompasses methods through which the research can be undertaken and the resultant artefacts evaluated. This methodology has a number of discrete stages: problem awareness; a core design-build-evaluate iterative cycle through which the research is conducted; and a conclusion stage. The design science research was conducted through the development of a number of artefacts, the prime being the 4D-Semantic Extract Load (4D-SETL) framework. The effectiveness of the framework was assessed by applying it to semantically interpret and integrate a number of large scale datasets and to instantiate a prototype graph database warehouse to persist the resultant ontology. A series of technical experiments confirmed that directly reflecting the model patterns of 4D ontology within a prototype data warehouse proved an effective means of both structuring and semantically integrating complex datasets and that the artefacts produced by 4D-SETL could function at scale. Through illustrative scenario, the effectiveness of the approach is described in relation to the ability of the framework to address a number of weaknesses in current approaches. Furthermore the major advantages of the 4D-SETL are elaborated; which include ability of the framework is to combine foundational, domain and instance level ontological models in a single coherent system that dispensed with much of the translation normally undertaken between conceptual, logical and physical data models. Additionally, adopting a perdurantist realist foundational ontology provided a clear means of establishing and maintaining the identity of physical objects as their constituent temporal and spatial parts unfold over the course of tim

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

A Survey of Top-Level Ontologies - to inform the ontological choices for a Foundation Data Model

The Centre for Digital Built Britain has been tasked through the Digital Framework Task Group to develop an Information Management Framework (IMF) to support the development of a National Digital Twin (NDT) as set out in “The Pathway to an Information Management Framework” (Hetherington, 2020). A key component of the IMF is a Foundation Data Model (FDM), built upon a top-level ontology (TLO), as a basis for ensuring consistent data across the NDT. This document captures the results collected from a broad survey of top-level ontologies, conducted by the IMF technical team. It focuses on the core ontological choices made in their foundations and the pragmatic engineering consequences these have on how the ontologies can be applied and further scaled. This document will provide the basis for discussions on a suitable TLO for the FDM. It is also expected that these top-level ontologies will provide a resource whose components can be harvested and adapted for inclusion in the FDM

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine