Ontology Construction Research Articles

Th eology as the science of existence and philosophy as the science of being in the fundamental ontology of M. Heidegger

The article deals with the project of understanding transcendental reality, undertaken within the framework of phenomenology. It is shown that phenomenological reduction, which leaves beyond the limits of evidence all types of objects of consciousness and all types of existence, if they do not reveal their meaning within the framework of the phenomenal givenness of themselves to experience, fundamentally does not allow us to talk about the transcendent. This circumstance reveals the limitations of phenomenology and the need to supplement it with various ontological and epistemological constructions, such as positive sciences, theology, theory of objects, ontology of ideal objects, etc. The article demonstrates a project for such an addition: the fundamental ontology of Martin Heidegger. Martin Heidegger shows that phenomenology in the format of fundamental ontology Dasein has an advantage over theology, since it is the science of being, and theology is just the science of existence, namely one of its types — historical revelation. It turns out that this project separates phenomenology and theology as the basis for any positive research, including theological, and thus does not allow us to talk about a phenomenological study of the transcendent. Thus, it is shown that phenomenological philosophy and theology are fundamentally incompatible.

Ontological model of structure and parameters of components in high-tech product modernization projects

The subject of the article is models representing the structure and parameters of components in high-tech products based on the analysis of technical documentation in modernization projects. The purpose of the proposed research is to enhance the quality of modernization processes of high-tech products by forming an ontological model of the product with innovative components, considering the diversity of information support. The article addresses the following tasks: studying the main methods and technologies of system representation of complex product structure; forming a model of structural-functional decomposition of a high-tech product, creating an ontological model of the structure and parameters of a high-tech product based on technical documentation. The following methods are applied: systemic approach, methods of functional-structural decomposition, set theory, ontology construction methods, semantic models. The following results were obtained: The main methods of system representation of complex product structure were investigated, based on the following principles: decomposition of complex product architecture, layering of complex product representation, multivariate synthesis of component architecture. The main directions and advantages of using 3D technologies for solving design tasks in modernization projects of high-tech product components are considered. The decomposition of a high-tech product into component parts and partial parameters is proposed, taking into account functional, structural, and parametric characteristics. An ontological model of the structure and parameters of a high-tech product is formed based on a set of technical documentation and considering additional heterogeneous sources of information. Innovative elements are identified, the description of which may be fuzzy. Conclusions. The proposed ontological model can serve as a basis for finding similar solutions regarding the design of innovative components in the precedent database, which is properly structured. In the absence of similar design solutions, innovative component design can be carried out using 3D technologies based on supplementing fuzzy information in the semantic model.

Identifying Terms to Represent Concepts of a Work Process Ontology

Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field. Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

New Trinitarian Ontologies? Trinitarian Theology, Theological Anthropology and Contemporary Critical Consciousness in Dialogue

AbstractThe recent translation into English of Klaus Hemmerle's Theses Towards a Trinitarian Ontology has led to a renewed interest in ontology and in the construction of new trinitarian ontologies. In his Theses, Hemmerle argues that a new trinitarian ontology discloses a new order of things: the analogy of Being becomes an analogy of the Trinity. A trinitarian ontology, therefore, turns on an axis of relationality and its impetus is reflexive and performative. In this article, I take up Hemmerle's argument that dialogue with theological anthropology is essential in the development of a trinitarian ontology. I engage the theological anthropologies of Kwok Pui‐Lan and Rita Nakashima Brock, whose work reflects a relational turn in theological anthropology, and bring these into dialogue with Hemmerle's insights. In doing so, I consider the implications of contemporary critical consciousness for thinking the human‐divine relationship and argue for a Christian trinitarian praxis which explicitly works to subvert narratives and structures that perpetuate the silencing of diverse discourses.

Approach to automating the construction and completion of ontologies in a scientific subject field

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.

Research on domain ontology construction based on the content features of online rumors

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

X-LoRA: Mixture of low-rank adapter experts, a flexible framework for large language models with applications in protein mechanics and molecular design

We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks. The design is inspired by the biological principles of universality and diversity, where neural network building blocks are reused in different hierarchical manifestations. Hence, the X-LoRA model can be easily implemented for any existing large language model without a need for modifications of the underlying structure. We develop a tailored X-LoRA model that offers scientific capabilities, including forward/inverse analysis tasks and enhanced reasoning capability, focused on biomaterial analysis, protein mechanics, and design. The impact of this work includes access to readily expandable and adaptable models with strong domain knowledge and the capability to integrate across areas of knowledge. Featuring experts in biology, mathematics, reasoning, bio-inspired materials, mechanics and materials, chemistry, protein biophysics, mechanics, and quantum-mechanics based molecular properties, we conduct a series of physics-focused case studies. We examine knowledge recall, protein mechanics forward/inverse tasks, protein design, adversarial agentic modeling including ontological knowledge graph construction, and molecular design. The model is capable not only of making quantitative predictions of nanomechanical properties of proteins or quantum mechanical molecular properties but also reasoning over the results and correctly predicting likely mechanisms that explain distinct molecular behaviors.

A new framework for building agricultural domain-based ontologies from text documents using natural language processing and artificial intelligence techniques

The advancements in technology, particularly in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) can be advantageous for the agricultural sector to enhance the yield. Establishing an agricultural ontology as part of the development would spur the expansion of cross-domain agriculture. Semantic and syntactic knowledge of the domain data is required for building such a domain-based ontology. To process the data from text documents, a standard technique with syntactic and semantic features are needed because the availability of pre-determined agricultural domain-based data is insufficient. In this research work, an Agricultural Ontologies Construction framework (AOC) is proposed for creating the agricultural domain ontology from text documents using NLP techniques with Robustly Optimized BERT Approach (RoBERTa) model and Graph Convolutional Network (GCN). The anaphora present in the documents are resolved to produce precise ontology from the input data. In the proposed AOC work, the domain terms are extracted using the RoBERTa model with Regular Expressions (RE) and the relationships between the domain terms are retrieved by utilizing the GCN with RE. When compared to other current systems, the efficacy of the proposed AOC method achieves an exceptional result, with precision and recall of 99.6% and 99.1% respectively.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Rethinking critical thinking, diversity and Indigenous awareness from a Blackfoot perspective

ABSTRACT Through this article, I critique the notion of critical thinking as it is mobilized in our education systems as an entry point for challenging the equity, diversity and inclusion lens that typifies professional development approaches which will illuminate why other approaches are needed. Using my own lived experiences as an Indigenous woman, I provide clarity on the concept of Indigenous relationality and the potential it has an alternate pathway for the embodiment of antiracist education that draws on Blackfoot ontological responsibilities and constructions of what it means to be a fully relational human being as understood from Blackfoot ways of knowing.

Innovative agricultural ontology construction using NLP methodologies and graph neural network

Advancements in technology brought various innovations to agricultural practices. As a part of the development, establishing an agricultural ontology would unleash the growth of cross-domain agriculture and Natural Language Processing (NLP). For constructing such domain-based ontology, semantic and syntactic understanding of the domain data is needed. In agriculture, the availability of pre-determined domain-based data is not sufficient hence, a standard methodology with syntactic and general semantic features are required for processing the data. In this research work, Agricultural Domain based Ontology Construction (ADOC) is proposed and the overall framework has three approaches for establishing the agriculture domain based ontologies. The input text documents undergo anaphora resolution phase utilizing the semantic-based method. In the first method of ADOC the ontology is developed using the terms and relationships that are extracted from the NLP techniques. The second method of ADOC uses pretrained BERT model and Hearst patterns while the third model of ADOC is based on pretrained BERT with regular expressions and unsupervised Graph Neural Network (GNN) for creating the agricultural ontology. The efficacy of the proposed ADOC utilizing BERT with regular expressions and GNN method shows an outstanding result when compared to other proposed and prevailing systems, with a precision and recall of 96.67% and 98.31%.

Systems and ontological analyses: similarities and differences be-tween the concepts

Terms, concepts and their content are not only the foundations of public communications, the keys to mutual understanding, knowledge transfer and cognition, but also fundamental elements in the construction of computer ontologies for artificial intelligence systems. The evolution of knowledge in subject areas leads, as a consequence, to the evolution of language, which is forced to change, borrowing words from other areas, expanding their meanings. The concept of systems analysis has a broad interpretation and is actively used in various fields of activity (science, economics, design, engineering, etc.), going deeper and adapting to specific subject and subject areas. The concepts related to system analysis (system, subject area, problem area, etc.) are also evolving. Systems analysis focuses on the relationships between elements of a system and determining its structure and functions; it includes various modeling and optimization methods and is used to solve specific problems and problems. Ontological analysis is associated with the study and description of objects, events, processes and relationships in the subject area, of which the system (or systems) may be a part; describes the structure and properties of the elements that make up the system and the relationships between them; used to create a formal description of a system and provide a general understanding of its components. The result of ontological analysis can be the basis for systems analysis or a part of it. Systems and ontological analyses are aimed at understand complex systems and their components and can be used together. The difference between these concepts is largely determined by the content of the field of study concepts, its boundaries, the object of research as a process, goal setting, the traditions and context that have developed among researchers in specific subject areas. Within the framework of the accepted nomenclature of scientific specialties of the Higher Attestation Commission, it can be argued that ontological analysis is included in the scientific specialty “Systems analysis, control and information processing, statistics.”

An ontology‐based semantic description model of ubiquitous map images

AbstractMap images with various themes and cartographic representations have become ubiquitous on the Internet. Such ubiquitously and openly accessible data, named ubiquitous map images in this study, are a potential resource for many geographic information applications such as cartographic design. However, there is a semantic gap between the simple physical form and the complex connotation of ubiquitous map images, which hinders their further applications. To mitigate such barrier, this article develops an ontology‐based semantic description model for ubiquitous map images. First, we discuss the design concerns and principles of the semantic description model of ubiquitous map images. Second, three semantic layers of the semantic description model are proposed, that is, image semantic description layer, cognitive tool layer, and information source layer, and detailed semantic description items are defined for each layer. Furthermore, a formalized semantic description model for ubiquitous map images is developed using ontology construction tools, which lays the foundation for automated and fine‐grained reasoning with the information embedded in map images. We construct a small test dataset consisting of weather maps, and use three types of constraints, namely “time‐topic,” “region‐topic,” and “map auxiliary elements” for the semantic retrieval experiments. The experiments show that the proposed semantic ontology model can enable complex semantic retrieval of ubiquitous map images. Finally, the scalability of the model is discussed from three perspectives: the depth of description, the combination with intelligent methods, and the integration with other open knowledge bases. The proposed model provides a semantic label system for applying data‐driven approaches to decode ubiquitous map images, which also paves the path to the development of cartographic theory in the era of information and communications technologies.

A knowledge graph and BiLSTM-CRF-enabled intelligent adaptive learning model and its potential application

The significant advancement of Artificial Intelligence (AI) technology has led to the gradual emergence of intelligent adaptive learning as a pivotal modality for acquiring knowledge. The knowledge graph, as a crucial component of the intelligent adaptive learning model, assumes the vital responsibility of guiding and facilitating the learning process while evaluating its outcomes. The current knowledge graph, however, presents several challenges including fragmented knowledge content, inadequate ability characterization, and limited automatic construction capabilities, which hinder its effectiveness in assisting learners to achieve their learning objectives. The paper proposes the knowledge-resource-objective (KRO) model of knowledge graph, based on a robust correlation between knowledge, learning resources, and learning objectives. In the KRO model, Bidirectional Encoder Representations from Transformers (BERT) is utilized for data pre-processing. Protége is employed for ontology construction. Entities and relations are synchronously annotated using the Main-Entity+ Relation +Begin-Inside-End-Single-Other (ME+R+BIESO) corpus labeling model to complete the triplet construction. Additionally, entities and relations are extracted synchronously utilizing a Bi-directional Long-Short Term Memory-Conditional Random Field (BiLSTM-CRF) model. Knowledge visualization and reasoning are achieved through Neo4j graph database. The study employs the Letter of Credit as a case study to examine the application of the KRO model, and assesses its efficacy through metrics such as accuracy, precision, recall, and F1-score. The research demonstrates that the KRO model effectively targets learning objectives, restructures knowledge and resources, optimizes learning pathways, provides recommendations for learning resources, and visualizes learners' cognitive states. As a result, it enhances the effectiveness of intelligent adaptive learning. The subsequent research will continue to focus on the visual representation of learners' learning progress and explore the practical application of learners' profiles in diverse disciplines, aiming to facilitate the realization of personalized learning for individuals.

Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data

The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen.

Ontologies of Herbal Medicine: A thematic review analysis from 2011-2022

This study examines various approaches for ontology construction and evaluation in medicinal plant ontology, focusing on the challenges associated with this domain. Thirty review articles on three databases revealed five alternative approaches for ontology generation in herbal medicine: well-structured methodology, customized methodology, semiautomatic approach, socio-technical approach, domain expert techniques, and top-level approach. However, 68% of the research did not include ontology evaluation. Ontology quality was often assessed using competency questions, expert opinion, and automated error detection. The study provides ontology developers with a better understanding of established methodologies for developing and evaluating standard ontologies in this area and identifies gaps in existing research for future studies.

Устойчивость и безопасность зеленых финансов с позиции многоагентных игр

Green finance increases the innovation potential of green transformation, helps to address climate change, the environmental crisis and energy security. It is important for green finance to achieve sustainable and balanced development, which will increase the security of both finance itself and many processes in the real economy that depend on it. The purpose of this study was to develop approaches to the ontological design of green finance and multi-agent modeling of the behavior of green finance actors, considering their sustainable and safe interaction. To achieve this goal it was necessary to clarify the conceptual apparatus of green finance and related areas; to characterize the "work" of the multi-agent model in the analysis of the behavior of the main green finance actors and to trace the prospects for their impact on the stability and security of the financial market; and finally, based on ontology construction and agent-based approach to nonlinear interactions in the green financial process, to determine the mechanism of building chains of the main market participants. The methodological basis of the study consists of general scientific research methods (analysis, synthesis, induction, deduction, logic), as well as evolutionary dynamics and agent-based approach to the analysis of financial markets. A comparative analysis of the works of various authors and the interpretation of theoretical provisions in related areas of knowledge about finance, as well as the analysis of their sustainable and safe development were carried out. As a result of the study, the authors identified the main approaches to the design of financial ontology and multi-agent modeling of financial interactions in the direction of determining their sustainable and safe development. The proposed model establishes that the criterion of the effectiveness of the applied strategy is the satisfaction with the state of financial impact on the economy, environment, and social sphere. Maintaining this state, along with saving the amount of resources used, is done through optimization. The chosen approach corresponds to the concept of sustainable development and safe functioning of the systems under consideration.

DarkOnto: An Ontology Construction Approach for Dark Web Community Discussions Through Topic Modeling and Ontology Learning

Social networks on the dark web are rich in data that provides valuable insight into the nature of the activities on the dark web and human behaviors related to these activities. It also encompasses a diversity of ideologies, interests, and thought patterns associated with illicit activities and businesses on the dark web. For this reason, social networks on the dark web constitute a powerful tool and a profuse data source for various investigative work. However, such investigations encounter considerable challenges related to the massive volumes of textual data, analyzing it effectively, and extracting knowledge from it. This knowledge can be used in various investigations and studies when representing it in ontologies as a unified and integrative data source. In this paper, we introduce a novel approach for extracting and representing knowledge hidden in dark web communities through topic modeling and ontology learning methods. We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class‐topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. Furthermore, we discuss the results, limitations, challenges, and future work. This paper presents a promising approach for extracting hidden valuable knowledge from dark web communities where investigating and conceptualizing criminal communities can be conducted efficiently.

Exploring the Feasibility of Integrating Marxist Philosophy into Teaching and Learning Reform in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Abstract This paper centers on the research of integrating the teaching reform of Marxist philosophy. The study first constructs the architecture of disciplinary knowledge mapping, focusing on the design of entity recognition based on the BiLSTM CRF model and the joint entity relationship extraction model based on annotation strategy. Then, Marxist philosophy is incorporated into the construction of disciplinary knowledge ontology. Finally, the discipline-specific course corpus and data layer are constructed to explore the feasibility and nature of integrating Marxist philosophy into discipline knowledge mapping for teaching reform. Feedback survey on the four dimensions of Marxist philosophy from the cognitive, affective, and attitudinal aspects, the difference between the mean and standard deviation of the data of the two classes is slight. The significance of the four dimensions (two-tailed) is more significant than 0.05, and this study is generally applicable to the teaching reform of colleges and universities. After the teaching reform, students’ online learning behaviors increased, and test scores improved significantly, i.e., The teaching reform in colleges and universities integrating Marxist philosophy is feasible and effective.

Fatahrafičny, relikvijny i naratyŭny miedyumy praz pryzmu faĺklarystyki (na sučasnym materyjalie Bieraciejščyny)

The article is devoted to the folkloristic consideration of the interdependent photographic, relic and narrative mediums, which in their existence are organically connected with oral history, family and clan stereotyped stories and various syntagmatically stable motifs of traditional folklore, including“magic” and fairy tale folklore. Based on the analysis of modern field material, it is argued that the statements recorded in the process of looking at photographs, relics and the corresponding commentary according to the cultural discourse and situational context are an ideal object of functional-pragmatic research relevant for modern folkloristics. During the joint viewing of photographs (usually with comments of the “knowers” addressed to the “questioner”) by representatives of different generations of a family and clan group, the history of the clan, intra-family knowledge (including about deceased ancestors), interrelated cultural, social and moral scenarios, certain motifs, plots and types of narratives, as well as the practices of seeing (as a socio-cultural action) and the corresponding narrativization associated with the ideology of visual and narrative are transmitted from the adults to the younger ones. As a result, not only identity formation, “initiation”, and socialization of the younger members of the family-clan group take place, but also the construction and maintenance of a vernacular ontology.