Domain Ontology Research Articles

Should the neuropsychoanalytic approach to the mind–body problem be re-framed in terms of transcendental idealism?

ABSTRACT The alignment of neuroscientific research with psychoanalytic theory and practice has led researchers in the emerging field of neuropsychoanalysis to scrutinize their metaphysical assumptions with respect to the relationship between mind and matter. The position which has achieved prevalence is dual-aspect monism. However, this trend is naïve to the fact that no consensus has been reached on the mind–body problem in the philosophy of mind literature. Moreover, like all metaphysical positions, dual-aspect monism contains conceptual flaws and is inconsistently defined, but these issues have not been sufficiently addressed in the neuropsychoanalytic literature. The present study reviews materialism, dual-aspect monism and transcendental idealism as candidate positions on which to ground neuropsychoanalytic thought. Whilst materialism holds that mind is ultimately reducible to material processes and dual-aspect monism holds that mind and matter are two irreducible perspectives on one underlying ontological domain, Kantian transcendental idealism views both mental and physical processes as subjective representations of a numinous reality which cannot be experienced directly. Instances are highlighted where neuropsychoanalytic dual-aspect monism slips into tacit forms of materialism and transcendental idealism. Transcendental idealism is conceived of as an epistemologically modest position which holds that inquiry into the mind–body relationship can only begin through exploring the boundaries of representation. It is submitted that the neuropsychoanalytic approach to the mind–body problem is implicitly aligned with transcendental idealism, and that this should be stated explicitly. Some clinical implications of these variations in metaphysical framing are discussed.

Метод разработки интеллектуальных тренажёров на основе онтологии предметной области

In academic disciplines, students are required to learn many new concepts, which necessitates extensive training with feedback. An intelligent simulator enables students to practice solving simple problems while receiving explanations for their mistakes, allowing teachers to focus on addressing more complex issues during lessons. This paper presents a method for developing intelligent simulators based on the ontology of the subject domain, implemented as web appli-cations suitable for both classroom and extracurricular use. Representing the problem and the subject area model in RDF format enables logical inference using the Apache Jena Reasoner inference engine. An example is provided of an intelligent simulator designed for learning the order of operations in expressions, supporting the C++, C#, and Python programming languages. The simulator can explain errors, generate explanatory hints, and engage in educational dia-logue through guiding questions. The simulator was tested with undergraduate and graduate students of the Faculty of Electronics and Computer Engineering at Volgograd State Technical University. Most students found the simulator to be more useful than traditional training tests. It can be employed both for independent study and as part of the educa-tional process in classroom settings.

Terminological Resources for Biologically Inspired Design and Biomimetics: Evaluation of the Potential for Ontology Reuse.

Biomimetics aims to learn from living systems to develop innovative technical artefacts. As it transcends disciplinary boundaries and needs to integrate both biological and technological knowledge, a domain ontology for biomimetics would be highly desirable. So far, several terminological resources have been designed to support the biomimetic development process. This paper examines nine resources for Biologically Inspired Design and biomimetics, including taxonomies, thesauri, and ontologies. Their benefits and limitations for structuring or organising biomimetic knowledge are evaluated against nine criteria, including availability, clarity, and machine readability. Our analysis shows that existing terminological resources have little to no potential for reuse due to inconsistent structure, ambiguous class labels, lack of standardisation, and lack of availability. Furthermore, no resource adequately represents biomimetic knowledge, as all resources suffer from limitations in content representation, reusability, or infrastructure. In particular, an adequate domain ontology for supporting biomimetic development is lacking; we discuss the desiderata for such an ontology.

An ontology to describe the damage process to built cultural heritage triggered by earthquakes: The CHEDO model

Information on cultural heritage is critical both to understand its value and to support management. Collecting, managing, visualizing, and sharing this information requires standardization. In the last decades, architecture has advanced in information management with the implementation of Building Information Modelling (BIM) methodology. The application of this methodology to cultural heritage has associated challenges due to the complexity and heterogeneity of the information. The integration of ontologies and BIM allows obtaining semantically enriched digital models, which has led to a growing interest in the field of architectural cultural heritage. In this article, the Cultural Heritage Earthquake Damage Ontology (CHEDO) model is presented. This ontology has been designed to document damage and loss caused by earthquakes in cultural heritage, through its integration into BIM systems interconnected with databases, although this integration is not addressed in the present work. The CHEDO model integrates terms and relationships to document the damage process in relation to the building characteristics (architectural, spatial, constructional, and geometrical) and the physical environment of the same. This model, by providing a comprehensive description of the building and its environment, allows for complete and accurate documentation of damage, which supports the planning of post-earthquake restoration and reconstruction. To facilitate interoperability with parallel ontologies in close domains of architectural cultural heritage, CHEDO was designed taking as reference the high-level ontology Conceptual Reference Model (CRM) developed by the International Committee for Documentation (CIDOC), of the International Council of Museums (ICOM). The proposed ontology is structured in four blocks: 'building and surrounding environment', to describe the building and its environment; 'building damage', to represent the damage and its causes; 'location', to describe the location of the building and the external processes; and 'terminology', to categorise and classify certain classes of the ontology.

Method for Creating Domain-Specific Dataset Ontologies from Text in Uncontrolled English

Abstract Automated understanding of activities in enterprises is challenging due to a lack of domain specifications and a lack of domain ontologies. The goal of this research is to develop a method to extract elements of domain-specific processes from textual documents in unstructured English and form domain dataset ontologies. In order to achieve the goal, the related work on discourse analysis and business process modelling have been considered. The prominent technologies for implementation of the proposed method are machine learning, including classification algorithms and natural language processing using a large language model. The first experimental results are presented, and further research is discussed. Potentially, the method proposed can be implemented as a part of some assisting tool for system analysts and can support an analysis of the domain-specific information by providing contextual information from this and potentially related domains.

Resolving the “three-fit” problems in enterprise information systems: a design science approach with ontological solutions

PurposeAddressing integrity, flexibility and interoperability challenges in enterprise information systems (EISs) is often hindered by the “three-fit” barrier, which encompasses vertical, horizontal and transversal fit problems. To overcome these obstacles, we propose solutions aimed at defining the business view of EIS. This study addresses these issues by proposing solutions tailored to the business view of EIS. Specifically, it introduces the core ontology of sensitive business processes (COSBP), a conceptual framework designed to formalize and define the multidimensional dimensions of sensitive business processes (SBPs). By providing a unified structure of central concepts and semantic relationships, COSBP enhances both knowledge management (KM) and business process management (BPM) in organizational contexts.Design/methodology/approachThis paper adopts the design science research methodology covering the phases of a design-oriented research project that develops new artifacts, such as the COSBP ontology, based on SBP modeling requirements. Following a formal multi-level, multi-component approach, COSBP is structured into sub-ontologies across different abstraction levels. Built upon the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) foundational ontology, COSBP integrates and extends core concepts from core domain ontologies in business processes. The framework specifies six key modeling dimensions of SBPs – functional, organizational, behavioral, informational, intentional and knowledge – each represented as a distinct class of ontological modules (OMs).FindingsCOSBP offers a semantically rich and precise framework for modeling SBPs, addressing complexity and ambiguity in conceptual modeling. It supports the creation of expressive and effective SBP models while enabling consensus-driven representation at a generic level. Additionally, COSBP serves as a foundation for extending modeling notations and developing tools that align with these notations. Its application in enterprise environments improves the integration, adaptability and interoperability of EISs, ultimately enhancing organizational processes and decision-making.Originality/valueThe development of the COSBP ontology holds considerable potential for application in various industries beyond its original focus on business process management and KM. The ontology’s capability to semantically model sensitive, knowledge-intensive and dynamic processes can be extended to other real-life scenarios in other complex domains and sectors – for example, finance and banking, government and public services, insurance, manufacturing and supply chain management, retail, E-commerce, logistics and transportation crisis management, government and public services, higher education and so on. By integrating artificial intelligence (AI) with the COSBP ontology, we aim to enable more intelligent decision-making, process monitoring and improved management of SBPs in knowledge-driven domains.

Domain Ontology-Driven Knowledge Graph Generation from Text

A knowledge graph serves as a unified and standardized representation for extracting and representing textual information. In the field of knowledge extraction and representation research, named entity recognition and relation extraction provide effective solutions for knowledge graph generation tasks. However, it is a challenge that lies in extracting domain-specific knowledge from the rich and general textual corpora and generating corresponding domain knowledge graphs to support domain-specific reasoning, question-answering, and decision-making tasks. The hierarchical domain knowledge representation model (i.e. domain ontology) provides a solution for this problem. Therefore, we propose an end-to-end approach based on domain ontology embedding and pre-trained language models for domain knowledge graph generation from text, which incorporates domain node recognition and domain relation extraction phases. We evaluated our domain ontology-driven model on the Wikidata-TekGen dataset and the DBpedia-WebNLG dataset, and the results indicate that our approach based on the pre-trained language models with fewer parameters compared with the baseline models has significantly contributed to the domain knowledge graph generation without prompts.

Robotics Classification of Domain Knowledge Based on a Knowledge Graph for Home Service Robot Applications

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet.

Research on aspect-based sentiment analysis of movie reviews based on deep learning

Aspect-based sentiment analysis aims to extract the sentiment polarity of different aspects within a text. In recent years, most methods have relied on pre-trained language models such as BERT and Roberta to learn semantic representations from the context. However, in texts with ambiguous sentiment expression, the absence of domain knowledge guidance may lead pre-trained language models to miss critical information, and the attention mechanism might incorrectly focus on text that is irrelevant to the aspect categories. To address these issues, this study integrates the ontology of movie reviews to construct an aspect-based sentiment analysis model based on the ERNIE(OMR-EBA). We annotated a new Chinese data set focused on movie reviews to evaluate the model’s performance. Experimental results show that our model achieves 86% accuracy in aspectual sentiment analysis, which is better than other baseline models. The movie review domain ontology and aspect-based sentiment analysis model proposed in this study can provide valuable reference and guidance for research in the field of online movie reviews. It can also help movie production teams understand genuine user sentiments, aiding in subsequent marketing and production efforts.

Battery testing ontology: An EMMO-based semantic framework for representing knowledge in battery testing and battery quality control

The demand for advanced battery management systems (BMSs) and battery test procedures is growing due to the rising importance of electric vehicles (EVs) and energy storage systems. The diversity of battery types, chemistries and application scenarios presents challenges in designing and optimizing BMSs and determining optimal battery test strategies. To address these challenges, semantic web technologies and ontologies offer a structured and common vocabulary for information sharing and reuse in battery management and testing. This work introduces the Battery Testing Ontology (BTO), a standardized, comprehensive, and semantically flexible framework for representing knowledge in electrical battery testing and quality control. BTO models a variety of electrical battery cell tests, specifying required test hardware and calibration procedures, mechanical fixturing of batteries, and referencing electrical measurement data. For example, it supports electrochemical impedance spectroscopy, self-discharge and high-voltage separator tests, the latter specifically demonstrating separator requirements, hardware specifications, and measurement details. Positioned within the ontology ecosystem of materials science, BTO aligns with the Elementary Multiperspective Material Ontology (EMMO) and related domain ontologies such as the Characterization Methodology Ontology (CHAMEO). This work elaborates on BTO’s development, structure, components and applications, highlighting its significant contributions to the field of battery testing.

Cephalic ganglia transcriptomics of the American cockroach Periplaneta americana (Blattodea: Blattidae).

The American cockroach Periplaneta americana (L.) (Blattodea, Blattidae) has been a model organism for biochemical and physiological study for almost a century, however, its use does not benefit from the genetic tools found in key model species such as Drosophila melanogaster. To facilitate the use of the cockroach as a model system in neuroscience and to serve as a foundation for functional and translational experimentation, a transcriptome of the cephalic ganglia was assembled and annotated, and differential expression profiles between these ganglia were assessed. The transcriptome assembly yielded >400 k transcripts, with >40 k putative coding sequences. Gene ontology and protein domain searches indicate the cerebral and gnathal ganglia (GNG) have distinct genetic expression profiles. The developmental Toll signaling pathway appears to be active in the adult central nervous system (CNS), which may suggest a separate role for this pathway besides innate immune activation or embryonic development. The catabolic glycolytic and citric acid cycle enzymes are well represented in both ganglia, but key enzymes are more highly expressed in the GNG. Both ganglia express gluconeogenic and trehaloneogenic enzymes, suggesting a larger role of the CNS in regulating hemolymph sugar homeostasis than previously appreciated. The annotation and quantification of the cephalic ganglia transcriptome reveal both canonical and novel pathways in signaling and metabolism in an adult insect and lay a foundation for future functional and genetic analysis.

Semantic Alignment in Disciplinary Tutoring System: Leveraging Sentence Transformer Technology

In this work, we present a disciplinary e-tutoring system that integrates ONTO-TDM, an ontology designed for teaching domain modeling, with advanced transformer technology. Our primary objective is to enhance semantic similarity tasks within the system by fine-tuning a Sentence Transformer model. By carefully adjusting training parameters with a curated dataset of question-answer pairs focused on algorithms and data structures, we achieved a notable improvement in system performance. The Sentence Transformer model, combined with domain ontology, achieved an accuracy of 91%, a precision of 93%, a recall of 89%, and an F1-score of 90%, significantly surpassing the results of existing works. This methodology highlights the potential to deliver personalized support and guidance in tutoring scenarios. It effectively addresses the evolving needs of modern education by offering tailored answers and reducing the necessity for constant learner-tutor interaction, thereby improving the efficiency of educational support systems.

Mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

BackgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts.We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.ResultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.ConclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

Ontology-based GraphQL server generation for data access and data integration

In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.

Ontology-Based Landslide Knowledge Representation and Hazard Assessment Research

The occurrence of landslides is characterized by suddenness and unpredictability, with many complex relationships among various influencing factors. This study, by introducing the ontology of knowledge engineering, summarizes the characteristics of landslide knowledge and constructs a multidisciplinary, multi-level, and multi-perspective ontology of the landslide domain. Through in-depth analysis of existing landslide research systems and methods, the study defines the basic concepts, attributes, and their complex relationships and rules related to landslides. This research expresses landslide knowledge using the Web Ontology Language (OWL), enabling effective storage and retrieval of landslide knowledge, thus promoting the systematization and standardization of knowledge in the landslide domain. Furthermore, leveraging the high knowledge density feature of the constructed landslide domain ontology, this study extends traditional landslide evaluation and prediction methods by introducing more evaluation indicators based on the constructed landslide ontology. The comprehensive evaluation of landslide risk is conducted using the contribution-weighting method, providing new insights and tools for landslide risk management and decision-making.

Separability and Its Approximations in Ontology-based Data Management

Given two datasets, i.e., two sets of tuples of constants, representing positive and negative examples, logical separability is the reasoning task of finding a formula in a certain target query language that separates them. As already pointed out in previous works, this task turns out to be relevant in several application scenarios such as concept learning and generating referring expressions. Besides, if we think of the input datasets of positive and negative examples as composed of tuples of constants classified, respectively, positively and negatively by a black-box model, then the separating formula can be used to provide global post-hoc explanations of such a model. In this paper, we study the separability task in the context of Ontology-based Data Management (OBDM), in which a domain ontology provides a high-level, logic-based specification of a domain of interest, semantically linked through suitable mapping assertions to the data source layer of an information system. Since a formula that properly separates (proper separation) two input datasets does not always exist, our first contribution is to propose (best) approximations of the proper separation, called (minimally) complete and (maximally) sound separations. We do this by presenting a general framework for separability in OBDM. Then, in a scenario that uses by far the most popular languages for the OBDM paradigm, our second contribution is a comprehensive study of three natural computational problems associated with the framework, namely Verification (check whether a given formula is a proper, complete, or sound separation of two given datasets), Existence (check whether a proper, or best approximated separation of two given datasets exists at all), and Computation (compute any proper, or any best approximated separation of two given datasets).

Modeling inter-organizational business process governance in the age of collaborative networks

Collaborative networks require inter-organizational business process governance (IO-BPG) mechanisms to define ownership over shared resources and activities, accountability over operations, inter-organizational roles and responsibilities, and strategic partner alignment. We developed an IO-BPG modeling approach aiming to incorporate (1) IT governance activities (e.g., IT performance measurement), (2) data governance activities (e.g., data strategy management), and (3) “shadow” parallel governance-related operations. Resulting from a design science research project, our contributions include the building blocks (domain attributes, ontology, and requirements) of a novel BPMN extension, its demonstration in logistics operations, its evaluation, and design principles to guide IO-BPG modeling. Suggestions for the development and evaluation of future BPMN extensions are also highlighted based on the lessons learned in this project. For practitioners, our contribution can improve accountability reports over data assets and operations, identify dataset ownership, assist in the coordination of governance activities in networked businesses, and comply with regulations and strategic partnership agreements.

Prophecy redux: a critique of Leanne Simpson’s concept of autogenocide

ABSTRACT This article examines the thought of Leanne Betasamosake Simpson as a case study in Indigenous decolonial theory. Primarily by engaging with Simpson’s approach in As We Have Always Done, it develops a critique of the concept of ‘autogenocide’. It argues that the liberatory potential of grounded normativity is undercut by Simpson’s juxtaposition of resistance with the immediate threat of self-destruction. This is signalled by the failure to exist in an adequately Indigenous ontological domain. The result arguably summons myths about Indigenous disappearance – that Indigenous peoples are a ‘dying race’ responsible for their own demise, and thus may read as injurious to Indigenous peoples who defy the markers of indigeneity outlined by Simpson in her Radical Resurgence Project. This critique is framed with reference to biopolitics, resilience, and Indigenous suicide. Transposing autogenocide to the Indigenous suicide crisis in Canada demands that the utility of the concept be evaluated in terms of another anti-colonial imaginary, where death by suicide exposes the structural violence of settler colonialism. It further contends that Critical Indigenous Studies is uniquely charged with confronting the intellectual, political, and philosophical consequences produced by the autogenocide concept. Most importantly, the alternative forms of essentialism and erasure that it heralds.

Multi‐Dimensional Knowledge System and Ontology Construction of Qi Culture from the Meme Perspective

ABSTRACTThis paper deconstructs the profound context of Qi Culture from the perspective of Meme and constructs a structured and systematic knowledge description framework. It is useful for exploring the core values and promoting the creative transformation and innovative development of Qi Culture. The research process involves collecting user discussions and research data about Qi Culture from multiple sources, and then using the BERTopic model and qualitative analysis methods to obtain diverse examples of Qi cultural elements. Afterwards, from the Meme perspective, Qi cultural memes are extracted to construct a domain ontology, thereby achieving multi‐dimensional semantic knowledge description of Qi Culture. This study helps to reveal the core elements of culture and their relationships, and will provide strong theoretical support for the integration and utilization of cultural resources.