Ontology Engineering Research Articles

Ontological Engineering in Complex Systems Based on Meta-Associative Graphs

Ontological Engineering in Complex Systems Based on Meta-Associative Graphs

Assessing American presidential candidates using principles of ontological engineering, word sense disambiguation, data envelope analysis and qualitative comparative analysis

Assessing American presidential candidates using principles of ontological engineering, word sense disambiguation, data envelope analysis and qualitative comparative analysis

A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.

Spline standard of mapped informativeness of correlation-extreme navigation as the potential of cybernetic situational awareness

A hypothesis about the possibility of using a spline standard of mapped informativeness of correlation-extreme navigation as a potential for cybernetic awareness is put forward. The expediency of understanding cybernetic awareness as an ontological engineering of the geometric implementation of a spline fragment of mapping to ensure the principle of alternative positioning is revealed. The forecast of the achievability of the effect of the vessel location predictability in the local environment of the satellite signals incapacity in the practical realization of the potential of situational awareness is made. The possibility of conceptual resistance of the developed algorithms of malicious virtual interference due to the unique composition of spline functions in assessing the vulnerability of the bridge onboard computers to a cybernetic threat is substantiated. Attention is focused on the fundamental impossibility of distorting the spline standard of informativeness to disrupt the classical process of linking the measured exposure of the navigation field to the reference one to ensure autonomous positioning of moving objects in any hacker attacks on the target mathematical asset. The main reason for the stability of approximation constructions, which lies in the piecewise architecture of the spline, when local violations of mathematical composition do not affect the overall task of forming a realistic navigation isosurface as a graphical structure of semantic modeling, is revealed. The proposals of effective information processing as attributes of cybernetic awareness are tested on the example of computer visualization of measuring fields of bathymetry and maps of magnetic anomalies in the form of fragments of threedimensional spreading of the planetary magnetic field. The developed package of application programs is adapted to synthesize a spline standard of mapped informativeness in the cybernetic awareness paradigm as an intellectual support for decision-making by a navigator in order to reduce his psychological cognitive load. It is proposed to use the potential of situational awareness as a mathematical support for an automated navigation system with artificial intelligence within the framework of the concept of unmanned navigation in strategic accordance with the A-Navigation project. The fundamental importance of a positive assessment of cybernetic awareness in promoting the practical use of autonomous marine surface vessels in modern navigation practice is determined.

NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.

Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets. Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.

An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT

With the development of digital city and smart city construction, the City Information Model (CIM) has played a critical role as a container of spatial–temporal data to establish the Digital Twin City. For a digital twin city, a virtual high-fidelity CIM model that corresponds closely to the real physical world is the premise and cornerstone of its construction. Therefore, the integration of BIM, GIS and IoT has become the preferred topic for researchers and has received much more attention from a wide academic circle. However, traditional integration mainly focuses on the conversion of both IFC and CityGML, and IoT data are also often used as visualizations. More importantly, the underlying data formats of GIS, BIM and IoT are still independent of each other without a unified data structure expression, so real data-driven analysis and decision-making cannot be implemented. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS and IoT data. First, the related work of BIM, GIS and IoT integration is studied and analyzed. A comparison of three mainstream approaches, data conversion, standard extension and data linking, is conducted, and it illustrates the advantages of ontology techniques in solving data interoperability problems. Second, a technical framework of BIM, GIS and IoT data integration based on ontology technology is proposed. The approach is mainly divided into five steps: geometry processing, data instantiation, ontology construction, ontology mapping and querying application. On the basis of the CIM ontology, an application ontology is built for a specific application domain to illustrate rule-based mapping, querying and inferring. Finally, the case study shows that the Ontology-based methodology in this paper has contributed to establish a general pattern for CIM data integration by mapping and linking concepts from the semantic level. It avoids changes in the original data sources and the missing data problem.

Emergency Decision Making for Electric Power Personal Accidents Based on Ontology and Case-Based Reasoning

Improvements in the emergency response efficiency and management level of power construction sites are conducive to reducing the construction safety risk of power infrastructure projects and then achieving the sustainability of construction site safety. Therefore, this paper proposes an emergency decision-making method for electric power personal accidents, which applies ontology and case-based reasoning to electric power emergency decision making. Firstly, ontology technology is used to structurally represent power accident case knowledge and clarify concepts and their relationships. Then, a power accident knowledge ontology hierarchy is designed, and a powerful personal accident case library is established. Secondly, by calculating cases’ conceptual similarity, attribute similarity, and structural similarity, a global power accident case similarity calculation method is proposed, and case matching is performed based on the calculation results to achieve case knowledge retrieval and reuse. Finally, the results of the example-based study show that the method effectively achieves the accurate retrieval of electric power accident cases, improves the efficiency of the emergency decision response to electric power construction site accidents, and then provides support for emergency decision making for electric power construction site accidents.

Unification of tsunami-related terminology: Ontology engineering perspective

Like many research areas, tsunami research has plenty of related topics that lack unified terminology. Some particular sub-topics may not have enough attention or do not share the same terminology as different views on the phenomenon. This issue can be tackled by an ontology that puts knowledge from different related topics into a formal structure that connects concepts with relationships. This paper proposes the development process of Tsunami-Related Ontology (TRO) that would aid the research in this field. The proposed semi-automatic ontology development methodology applies to any research field and does not require specific algorithms or programming skills to achieve its goal. This paper particularly focuses on three research gaps related to tsunami that are expected to benefit significantly from an ontology: meteorological tsunami, community resilience, and physical vulnerability. For these topics, the created ontology provides a formal taxonomy that links individual concepts to equivalent or related concepts, providing an easy-to-understand overview of the area.

Ontology Development for Creating Identical Software Environments to Improve Learning Outcomes in Higher Education Institutions

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.

Application of ontology-based engineering and stem approach in learning

Currently, there is a need to improve the educational system and develop interdisciplinary studies at all stages of education, from school to postgraduate education. The implementation of cross-curriculum connections promotes a holistic view of natural phenomena and the relationship between them, that is, this knowledge becomes more meaningful and applicable in practice. The article proposes an approach to building a conceptual model of the content of education in the form of a thesaurus and ontology, the use of which will ensure that the educational information is adaptively selected and put straight. The article discusses the possibility of and experience in using ontological modeling and engineering for the conceptual description of school and higher education. The article discusses the development of the ontological science, technology, engineering, and mathematics (STEM) Education at School model. The article builds an ontological model, which is an integration of the ontology of school and the ontology of university. When filling in the knowledge base, it becomes possible to identify interdisciplinary relationships. The use of ontological engineering methods will improve the quality of education for schoolchildren and students through the semantic description of knowledge of the subject area by using interdisciplinary and STEM approaches in the process of education.

APPLICATION OF ONTOLOGICAL ENGINEERING METHODS IN OPTIMIZATION OF COMPLEX ML MODELS AND AI APPLICATIONS

In today's world, artificial intelligence (AI) and machine learning (ML) play a key role in many areas of human activity. However, the complexity and insufficient definition of the relevant models can create problems of their efficiency, interpretability and management when implementing technologies based on AI and ML. That is why it is important to use ontological engineering methods to improve these models. Ontological engineering is an approach that makes it possible to represent knowledge and dependencies between them in special information structures, so-called ontologies. Ontologies define concepts, relationships, and rules that describe a domain or problem area. At the same time, these information structures, in comparison with others, have extended functionality, which brings the manipulation of ontologies closer to the human thinking process.

A novel agile ontology engineering methodology for supporting organizations in collaborative ontology development

Ontologies can represent technological enablers for knowledge elicitation and management in different kinds of organizations, especially with the exponential growth of sources and types of data fostered by digital transformation. However, their adoption in business applications is still limited, with existing Ontology Engineering Methodologies (OEMs) lacking adequate support during knowledge elicitation, authoring and reuse phases. This paper introduces a novel agile ontology engineering methodology (AgiSCOnt) to support ontologists (especially novice ones) in ontology development workflow, fostering collaboration with domain experts in an iterative, flexible and customizable approach. AgiSCOnt combines macro-level instructions with micro-level guidance, leveraging existing techniques and a management framework to help novice ontologists throughout the whole ontology engineering process. The methodology is compared to existing OEMs and assessed with three other agile methodologies (UPONLite, SAMOD, and RapidOWL). The evaluation is conducted with a sample of novice ontologists in a learning environment on Industry 4.0 technologies. Both the development process with a methodology from a user perspective and the quality of the developed ontologies were considered in the evaluation. Preliminary results show that AgiSCOnt effectively supports authoring and reuse, with developed ontologies of good quality. It is perceived as clear and simple, while being flexible and adaptable enough, thus supporting knowledge management and sharing in industrial organizations through the documentation of the ontologies.

Sprachdatenbasierte Modellierung von Wissensnetzen in der mittelalterlichen Romania (ALMA): Projektskizze

Abstract We present ALMA, a new research project aimed at investigating the interaction between language and knowledge practices from AD 1100 to 1500. Our primary question is how Medieval Italian, French, Occitan, Catalan, and Spanish developed into languages of knowledge and scholarship (German Wissen(schafts)sprachen) in permanent opposition to and exchange with the predominant Latin (but also with Arab, Greek, and Hebrew). Focusing on two domains, medicine and law, the project combines linguistics, text philology, and the history of science with the Digital Humanities and ontology engineering. ALMA will create two multi-lingual, domain-specific text corpora by integrating text editions of hitherto unedited manuscripts and incunabula, and digitized printed editions. Our corpus-linguistic exploration of the ALMA corpora will provide the basis for lexical-semantic studies that analyze emerging knowledge networks and the depth of their linguistic representations. We hypothesize that language evolution and the development of more complex linguistic structures will allow for measuring the impact of knowledge practices on medieval vernacular languages. We will trace the dissemination of lexical material across languages, language varieties, cultural spaces, and periods. This will enable us to follow specific vernacular communication channels. We will use cutting-edge technologies to compile, publish, and share our findings, and to model them in the form of historicized ontologies and Linked Data. Our onomasiological, ontology-driven approach will result in the creation of domain models that can be re-used within the Semantic Web. This has great potential to be relevant for researchers from different disciplines.

Identification of Key Elements in Prostate Cancer for Ontology Building via a Multidisciplinary Consensus Agreement.

Clinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research. A modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM). The panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM. This expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).

ScrumOntoSPL: Collaborative Method of Agile Product Line Engineering for Software Resource Reuse

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE’s characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market.

ASPER: Attention-based approach to extract syntactic patterns denoting semantic relations in sentential context

Semantic relationships, such as hyponym–hypernym, cause–effect, meronym–holonym etc., between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations—hyponym–hypernym, cause–effect, and meronym–holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER’s performance is substantially superior.

Cybernetics and Directed Evolution

Results. The place and role of cybernetics methods for solving the global problem of directed evolution are considered. The author investigates the eventual phenomenon of the interaction of two coryphaei of Ukrainian science V.I. Vernadsky and V.M. Glushkov in the formation of these scientific fields and the synergistic effect of this interaction. A special attention is paid to the applied aspects of using the scientific theories when implementing complex topical processes at the state and global levels. The list of the applied fields of cybernetics with their implementation in the projects of the Institute of Cybernetics under the leadership of V.M. Glushkov is endless, and his scientific ideas are successfully brought to life today. Keywords: cognitive intelligent technology, transdisciplinary scientific research (TSR), systemology of TSR, scientific picture of the world, directed evolution, noospheric theory, ontological engineering, convergence of technologies, consolidated intelligence, collective consciousness, research design, eventual analysis.

An Ontology-based Engineering system to support aircraft manufacturing system design

During the conceptual design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated against performance indicators in a collaborative engineering process. Domain experts’ knowledge and the motivations for decision-making is a crucial asset for enterprises which is challenging to be captured and capitalised. Ontology-based Engineering (OBE) systems emerge as a new generation of Knowledge-based Engineering techniques with advancements of ontology engineering methods and computer science technologies. Ontologies enable to capture both explicit and implicit domain knowledge from historical records and domain experts. These Ontology-based Engineering systems can stand highly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools. This paper proposes a tradespace framework with Ontology-based Engineering features included on top of existing Model-Based System Engineering and interoperability capabilities. These additional Ontology-based Engineering features reuse formalised knowledge via knowledge graph technologies and generative algorithms, changing the cognitive process from the designer, to an automatic process which generates design alternatives for the designer. The tradespace framework is demonstrated in a case study to design the aircraft fuselage orbital joint process, helping the designer to take better strategic decisions at conceptual phase and proving to be an advantageous paradigm for the design process.

A systematic comparison and evaluation of building ontologies for deploying data-driven analytics in smart buildings

Ontologies play a critical role in data exchange, information integration, and knowledge sharing across diverse smart building applications. Yet, semantic differences between the prevailing building ontologies hamper their purpose of bringing data interoperability and restrict the ability to reuse building ontologies in real-world applications. In this paper, we propose and adopt a framework to conduct a systematic comparison and evaluation of four popular building ontologies (Brick Schema, RealEstateCore, Project Haystack, and Digital Buildings) from both axiomatic design and assertions in a use case, namely the Terminological Box (TBox) evaluation and the Assertion Box (ABox) evaluation. In the TBox evaluation, we use the SQuaRE-based Ontology Quality Evaluation (OQuaRE) framework and concede that Project Haystack and Brick Schema are more compact with respect to the ontology axiomatic design. In the ABox evaluation, we apply an empirical study with sample building data that suggests Brick Schema and RealEstateCore have greater completeness and expressiveness in capturing the main concepts and relations within the building domain. The results indicate that there is no universal building ontology for integrating Linked Building Data (LBD). We also discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.

Modular ontology modeling

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.